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No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
  • Contents
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  • Contents
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  • Contents
    • @@ -277,20 +275,21 @@ wavelets, splines etc.

    If our feature space is not easy to separate, as shown in the figure -<<<<<<< HEAD -here, we can achieve a better separation by introducing more complex -basis functions. The ideal would be, as shown in the next figure, to, +generated by the code below, we can achieve a better separation by introducing a more complex +basis functions. The ideal would be, as shown by the code example below, to, via a specific transformation to obtain a separation between the -classes which is almost linear. -====

    === -here generated by the code below (see also Figures 12.2 and 12.3 of Hastie et al.), we can achieve a better separation by introducing more complex -basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to -obtain a separation between the classes which is almost linear. ->>>>>>> origin/master +classes that is almost linear. See also Figures 12.2 and 12.3 of Hastie et al..

    -The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same type of equations to be solved, except that -we need to introduce, for example, a polynomial transformation to a two-dimensional training set. +We can achieve a better separation by introducing more complex +basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to +obtain a separation between the classes which is almost linear. + +

    +The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same +type of equations to be solved, except that we need to introduce, for +example, a polynomial transformation to a two-dimensional training +set.

    diff --git a/doc/pub/week48/html/._week48-bs006.html b/doc/pub/week48/html/._week48-bs006.html index 1fdaed675..f6c904588 100644 --- a/doc/pub/week48/html/._week48-bs006.html +++ b/doc/pub/week48/html/._week48-bs006.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    The equations

    +

    The equations

    Suppose we define a polynomial transformation of degree two only. We define a vector \( \boldsymbol{x}_i=[x_i,y_i] \) and have diff --git a/doc/pub/week48/html/._week48-bs007.html b/doc/pub/week48/html/._week48-bs007.html index 5231d570b..cba270fd8 100644 --- a/doc/pub/week48/html/._week48-bs007.html +++ b/doc/pub/week48/html/._week48-bs007.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    The problem to solve

    +

    The problem to solve

    Using our definition of the kernel, we can rewrite again the Lagrangian diff --git a/doc/pub/week48/html/._week48-bs008.html b/doc/pub/week48/html/._week48-bs008.html index 310a4668d..bbab9d524 100644 --- a/doc/pub/week48/html/._week48-bs008.html +++ b/doc/pub/week48/html/._week48-bs008.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Tailoring the equations to the usage of CVXOPT

    +

    Tailoring the equations to the usage of CVXOPT

    We can rewrite this (see the solutions below) in terms of a convex optimization problem of the type diff --git a/doc/pub/week48/html/._week48-bs009.html b/doc/pub/week48/html/._week48-bs009.html index 57c30db46..0540aaacf 100644 --- a/doc/pub/week48/html/._week48-bs009.html +++ b/doc/pub/week48/html/._week48-bs009.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Different kernels and Mercer's theorem

    +

    Different kernels and Mercer's theorem

    There are several popular kernels being used. These are diff --git a/doc/pub/week48/html/._week48-bs010.html b/doc/pub/week48/html/._week48-bs010.html index 463aa5f5a..ba59324f5 100644 --- a/doc/pub/week48/html/._week48-bs010.html +++ b/doc/pub/week48/html/._week48-bs010.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    The moons example (Adapted from Geron, chapter 5)

    +

    The moons example (Adapted from Geron, chapter 5)

    diff --git a/doc/pub/week48/html/._week48-bs011.html b/doc/pub/week48/html/._week48-bs011.html index 454ebb6fd..28835354b 100644 --- a/doc/pub/week48/html/._week48-bs011.html +++ b/doc/pub/week48/html/._week48-bs011.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Mathematical optimization of convex functions

    +

    Mathematical optimization of convex functions

    A mathematical (quadratic) optimization problem, or just optimization problem, has the form diff --git a/doc/pub/week48/html/._week48-bs012.html b/doc/pub/week48/html/._week48-bs012.html index 3917e8304..f67f8be3c 100644 --- a/doc/pub/week48/html/._week48-bs012.html +++ b/doc/pub/week48/html/._week48-bs012.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    How do we solve these problems?

    +

    How do we solve these problems?

    If we use Python as programming language and wish to venture beyond diff --git a/doc/pub/week48/html/._week48-bs013.html b/doc/pub/week48/html/._week48-bs013.html index b08178c2a..1c2e7f207 100644 --- a/doc/pub/week48/html/._week48-bs013.html +++ b/doc/pub/week48/html/._week48-bs013.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    A simple example

    +

    A simple example

    We remind ourselves about the general problem we want to solve diff --git a/doc/pub/week48/html/._week48-bs014.html b/doc/pub/week48/html/._week48-bs014.html index cd139ebae..429bfca1f 100644 --- a/doc/pub/week48/html/._week48-bs014.html +++ b/doc/pub/week48/html/._week48-bs014.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Back to the more realistic cases

    +

    Back to the more realistic cases

    We are now ready to return to our setup of the optmization problem for a more realistic case. Introducing the slack parameter \( C \) we have diff --git a/doc/pub/week48/html/._week48-bs015.html b/doc/pub/week48/html/._week48-bs015.html index 669c281b9..4e68c1f91 100644 --- a/doc/pub/week48/html/._week48-bs015.html +++ b/doc/pub/week48/html/._week48-bs015.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Setting up the matrices and the problem

    +

    Setting up the matrices and the problem

    We have the general problem diff --git a/doc/pub/week48/html/._week48-bs016.html b/doc/pub/week48/html/._week48-bs016.html index e70cc6968..79cf26221 100644 --- a/doc/pub/week48/html/._week48-bs016.html +++ b/doc/pub/week48/html/._week48-bs016.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,10 +263,10 @@ MathJax.Hub.Config({ -

    Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

    +

    Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

    -We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the contraint +We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the constraint \( \ge \) with \( -1 \) in order to get \( \le \). It means that the problem \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \) can be written as $$ @@ -288,7 +286,7 @@ $$ \lambda_3 \\ \dots \\ \lambda_n \\ -\end{bmatrix}\wedge +\end{bmatrix} \preceq \begin{bmatrix} 0 \\ 0 \\ 0 \\ diff --git a/doc/pub/week48/html/._week48-bs017.html b/doc/pub/week48/html/._week48-bs017.html index 09a64b919..1d1452c03 100644 --- a/doc/pub/week48/html/._week48-bs017.html +++ b/doc/pub/week48/html/._week48-bs017.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    SVMs and Regression and multiclass classification

    +

    SVMs and Regression and multiclass classification

    More text will be added here. See also Hastie et al. section 12.3. diff --git a/doc/pub/week48/html/._week48-bs018.html b/doc/pub/week48/html/._week48-bs018.html index e9fec8087..b6211daff 100644 --- a/doc/pub/week48/html/._week48-bs018.html +++ b/doc/pub/week48/html/._week48-bs018.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Summary of course

    +

    Summary of course

    diff --git a/doc/pub/week48/html/._week48-bs019.html b/doc/pub/week48/html/._week48-bs019.html index f7ebedd44..47d1d1f1a 100644 --- a/doc/pub/week48/html/._week48-bs019.html +++ b/doc/pub/week48/html/._week48-bs019.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    What? Me worry? No final exam in this course!

    +

    What? Me worry? No final exam in this course!





    diff --git a/doc/pub/week48/html/._week48-bs020.html b/doc/pub/week48/html/._week48-bs020.html index 5d9dfbf78..2883650a3 100644 --- a/doc/pub/week48/html/._week48-bs020.html +++ b/doc/pub/week48/html/._week48-bs020.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? 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No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Topics we have covered this year

    +

    Topics we have covered this year

    The course has two central parts diff --git a/doc/pub/week48/html/._week48-bs021.html b/doc/pub/week48/html/._week48-bs021.html index 21312ef7a..5a569b717 100644 --- a/doc/pub/week48/html/._week48-bs021.html +++ b/doc/pub/week48/html/._week48-bs021.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Statistical analysis and optimization of data

    +

    Statistical analysis and optimization of data

    The following topics have been discussed: diff --git a/doc/pub/week48/html/._week48-bs022.html b/doc/pub/week48/html/._week48-bs022.html index 56a8ec66f..7c1b9ac88 100644 --- a/doc/pub/week48/html/._week48-bs022.html +++ b/doc/pub/week48/html/._week48-bs022.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Machine learning

    +

    Machine learning

    The following topics will be covered diff --git a/doc/pub/week48/html/._week48-bs023.html b/doc/pub/week48/html/._week48-bs023.html index df503077e..360fb0781 100644 --- a/doc/pub/week48/html/._week48-bs023.html +++ b/doc/pub/week48/html/._week48-bs023.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Learning outcomes and overarching aims of this course

    +

    Learning outcomes and overarching aims of this course

    The course introduces a variety of central algorithms and methods diff --git a/doc/pub/week48/html/._week48-bs024.html b/doc/pub/week48/html/._week48-bs024.html index 1b0ec98ef..150c9616a 100644 --- a/doc/pub/week48/html/._week48-bs024.html +++ b/doc/pub/week48/html/._week48-bs024.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

  • Contents
    • @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

    Perspective on Machine Learning

    +

    Perspective on Machine Learning

    1. Rapidly emerging application area
      2. diff --git a/doc/pub/week48/html/._week48-bs025.html b/doc/pub/week48/html/._week48-bs025.html index a460bfd6c..b89cbf46b 100644 --- a/doc/pub/week48/html/._week48-bs025.html +++ b/doc/pub/week48/html/._week48-bs025.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
    2. Contents
      3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

      Machine Learning Research

      +

      Machine Learning Research

      Where to find recent results: diff --git a/doc/pub/week48/html/._week48-bs026.html b/doc/pub/week48/html/._week48-bs026.html index c37cadf75..68bbcae8b 100644 --- a/doc/pub/week48/html/._week48-bs026.html +++ b/doc/pub/week48/html/._week48-bs026.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

    3. Contents
      4. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

      Starting your Machine Learning Project

      +

      Starting your Machine Learning Project

      1. Identify problem type: classification, regression
        2. diff --git a/doc/pub/week48/html/._week48-bs027.html b/doc/pub/week48/html/._week48-bs027.html index 9ce4440a4..4ed730da5 100644 --- a/doc/pub/week48/html/._week48-bs027.html +++ b/doc/pub/week48/html/._week48-bs027.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
      2. Contents
        3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

        Choose a Model and Algorithm

        +

        Choose a Model and Algorithm

        1. Supervised?
          2. diff --git a/doc/pub/week48/html/._week48-bs028.html b/doc/pub/week48/html/._week48-bs028.html index 56eb3249b..0bef89960 100644 --- a/doc/pub/week48/html/._week48-bs028.html +++ b/doc/pub/week48/html/._week48-bs028.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
        2. Contents
          3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

          Preparing Your Data

          +

          Preparing Your Data

          1. Shuffle your data
            2. diff --git a/doc/pub/week48/html/._week48-bs029.html b/doc/pub/week48/html/._week48-bs029.html index ee26c6138..c66d9ad97 100644 --- a/doc/pub/week48/html/._week48-bs029.html +++ b/doc/pub/week48/html/._week48-bs029.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
          2. Contents
            3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

            Which Activation and Weights to Choose in Neural Networks

            +

            Which Activation and Weights to Choose in Neural Networks

            1. RELU? ELU?
              2. diff --git a/doc/pub/week48/html/._week48-bs030.html b/doc/pub/week48/html/._week48-bs030.html index e03003db1..7fa6c2d5b 100644 --- a/doc/pub/week48/html/._week48-bs030.html +++ b/doc/pub/week48/html/._week48-bs030.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
            2. Contents
              3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

              Optimization Methods and Hyperparameters

              +

              Optimization Methods and Hyperparameters

              1. Stochastic gradient descent diff --git a/doc/pub/week48/html/._week48-bs031.html b/doc/pub/week48/html/._week48-bs031.html index 83d1faf13..e822afb81 100644 --- a/doc/pub/week48/html/._week48-bs031.html +++ b/doc/pub/week48/html/._week48-bs031.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
              2. Contents
                3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                Resampling

                +

                Resampling

                When do we resample? diff --git a/doc/pub/week48/html/._week48-bs032.html b/doc/pub/week48/html/._week48-bs032.html index eee60662f..19330bd4a 100644 --- a/doc/pub/week48/html/._week48-bs032.html +++ b/doc/pub/week48/html/._week48-bs032.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

              3. Contents
                4. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                Other courses on Data science and Machine Learning at UiO

                +

                Other courses on Data science and Machine Learning at UiO

                The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. diff --git a/doc/pub/week48/html/._week48-bs033.html b/doc/pub/week48/html/._week48-bs033.html index 8dbccf020..987e1f5a3 100644 --- a/doc/pub/week48/html/._week48-bs033.html +++ b/doc/pub/week48/html/._week48-bs033.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

              4. Contents
                5. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                Additional courses of interest

                +

                Additional courses of interest

                1. STK4051 Computational Statistics
                  2. diff --git a/doc/pub/week48/html/._week48-bs034.html b/doc/pub/week48/html/._week48-bs034.html index ade16a885..ea028807d 100644 --- a/doc/pub/week48/html/._week48-bs034.html +++ b/doc/pub/week48/html/._week48-bs034.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
                2. Contents
                  3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                  What's the future like?

                  +

                  What's the future like?

                  Based on multi-layer nonlinear neural networks, deep learning can diff --git a/doc/pub/week48/html/._week48-bs035.html b/doc/pub/week48/html/._week48-bs035.html index 7ade55e80..5864ed1ad 100644 --- a/doc/pub/week48/html/._week48-bs035.html +++ b/doc/pub/week48/html/._week48-bs035.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                3. Contents
                  4. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                  Types of Machine Learning, a repetition

                  +

                  Types of Machine Learning, a repetition

                  diff --git a/doc/pub/week48/html/._week48-bs036.html b/doc/pub/week48/html/._week48-bs036.html index fe616eb5f..3594423f2 100644 --- a/doc/pub/week48/html/._week48-bs036.html +++ b/doc/pub/week48/html/._week48-bs036.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
                4. Contents
                  5. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                  Why Boltzmann machines?

                  +

                  Why Boltzmann machines?

                  What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. diff --git a/doc/pub/week48/html/._week48-bs037.html b/doc/pub/week48/html/._week48-bs037.html index 725685a17..4e090ee6d 100644 --- a/doc/pub/week48/html/._week48-bs037.html +++ b/doc/pub/week48/html/._week48-bs037.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                5. Contents
                  6. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                  Boltzmann Machines

                  +

                  Boltzmann Machines

                  Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? diff --git a/doc/pub/week48/html/._week48-bs038.html b/doc/pub/week48/html/._week48-bs038.html index ce872241d..21281a005 100644 --- a/doc/pub/week48/html/._week48-bs038.html +++ b/doc/pub/week48/html/._week48-bs038.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                6. Contents
                  7. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                  Some similarities and differences from DNNs

                  +

                  Some similarities and differences from DNNs

                  1. Both use gradient-descent based learning procedures for minimizing cost functions
                    2. diff --git a/doc/pub/week48/html/._week48-bs039.html b/doc/pub/week48/html/._week48-bs039.html index 8bf80cb94..c598c732a 100644 --- a/doc/pub/week48/html/._week48-bs039.html +++ b/doc/pub/week48/html/._week48-bs039.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
                  2. Contents
                    3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    Boltzmann machines (BM)

                    +

                    Boltzmann machines (BM)

                    diff --git a/doc/pub/week48/html/._week48-bs040.html b/doc/pub/week48/html/._week48-bs040.html index f0b2f1dcb..602e6d4a8 100644 --- a/doc/pub/week48/html/._week48-bs040.html +++ b/doc/pub/week48/html/._week48-bs040.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? 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No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
                  3. Contents
                    4. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    A standard BM setup

                    +

                    A standard BM setup

                    diff --git a/doc/pub/week48/html/._week48-bs041.html b/doc/pub/week48/html/._week48-bs041.html index 42f711d44..34aeb4728 100644 --- a/doc/pub/week48/html/._week48-bs041.html +++ b/doc/pub/week48/html/._week48-bs041.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? 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                  4. Contents
                    5. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    The structure of the RBM network

                    +

                    The structure of the RBM network





                    diff --git a/doc/pub/week48/html/._week48-bs042.html b/doc/pub/week48/html/._week48-bs042.html index 2fac7ecf7..9369de6f6 100644 --- a/doc/pub/week48/html/._week48-bs042.html +++ b/doc/pub/week48/html/._week48-bs042.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
                  5. Contents
                    6. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    The network

                    +

                    The network

                    The network layers: diff --git a/doc/pub/week48/html/._week48-bs043.html b/doc/pub/week48/html/._week48-bs043.html index ec9a53355..361d99500 100644 --- a/doc/pub/week48/html/._week48-bs043.html +++ b/doc/pub/week48/html/._week48-bs043.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                  6. Contents
                    7. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    Goals

                    +

                    Goals

                    The goal of the hidden layer is to increase the model's expressive diff --git a/doc/pub/week48/html/._week48-bs044.html b/doc/pub/week48/html/._week48-bs044.html index eaae72d3c..2548b2bb2 100644 --- a/doc/pub/week48/html/._week48-bs044.html +++ b/doc/pub/week48/html/._week48-bs044.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                  7. Contents
                    8. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    Joint distribution

                    +

                    Joint distribution

                    The restricted Boltzmann machine is described by a Boltzmann distribution diff --git a/doc/pub/week48/html/._week48-bs045.html b/doc/pub/week48/html/._week48-bs045.html index 9e9897cd2..316737ec3 100644 --- a/doc/pub/week48/html/._week48-bs045.html +++ b/doc/pub/week48/html/._week48-bs045.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                  8. Contents
                    9. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    Network Elements, the energy function

                    +

                    Network Elements, the energy function

                    The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a diff --git a/doc/pub/week48/html/._week48-bs046.html b/doc/pub/week48/html/._week48-bs046.html index cbc96eb58..cdcfc7d45 100644 --- a/doc/pub/week48/html/._week48-bs046.html +++ b/doc/pub/week48/html/._week48-bs046.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                  9. Contents
                    10. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    Defining different types of RBMs

                    +

                    Defining different types of RBMs

                    There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \).

                    diff --git a/doc/pub/week48/html/._week48-bs047.html b/doc/pub/week48/html/._week48-bs047.html index 24c31ff2d..7d38d6078 100644 --- a/doc/pub/week48/html/._week48-bs047.html +++ b/doc/pub/week48/html/._week48-bs047.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                  10. Contents
                    11. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                    More about RBMs

                    +

                    More about RBMs

                    1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
                      2. diff --git a/doc/pub/week48/html/._week48-bs048.html b/doc/pub/week48/html/._week48-bs048.html index 3d2f71ff5..65dc84403 100644 --- a/doc/pub/week48/html/._week48-bs048.html +++ b/doc/pub/week48/html/._week48-bs048.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
                    2. Contents
                      3. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Autoencoders: Overarching view

                      +

                      Autoencoders: Overarching view

                      Autoencoders are artificial neural networks capable of learning diff --git a/doc/pub/week48/html/._week48-bs049.html b/doc/pub/week48/html/._week48-bs049.html index 247e4704b..5f077ec08 100644 --- a/doc/pub/week48/html/._week48-bs049.html +++ b/doc/pub/week48/html/._week48-bs049.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    3. Contents
                      4. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Bayesian Machine Learning

                      +

                      Bayesian Machine Learning

                      This is an important topic if we aim at extracting a probability diff --git a/doc/pub/week48/html/._week48-bs050.html b/doc/pub/week48/html/._week48-bs050.html index 7f2205c2b..be619028e 100644 --- a/doc/pub/week48/html/._week48-bs050.html +++ b/doc/pub/week48/html/._week48-bs050.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    4. Contents
                      5. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Reinforcement Learning

                      +

                      Reinforcement Learning

                      Reinforcement Learning (RL) is one of the most exciting fields of diff --git a/doc/pub/week48/html/._week48-bs051.html b/doc/pub/week48/html/._week48-bs051.html index 40f3eee1f..bbd632634 100644 --- a/doc/pub/week48/html/._week48-bs051.html +++ b/doc/pub/week48/html/._week48-bs051.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    5. Contents
                      6. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Transfer learning

                      +

                      Transfer learning

                      The goal of transfer learning is to transfer the model or knowledge diff --git a/doc/pub/week48/html/._week48-bs052.html b/doc/pub/week48/html/._week48-bs052.html index 636a7b6bb..9f27d7836 100644 --- a/doc/pub/week48/html/._week48-bs052.html +++ b/doc/pub/week48/html/._week48-bs052.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    6. Contents
                      7. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Adversarial learning

                      +

                      Adversarial learning

                      The conventional deep generative model has a potential problem: the diff --git a/doc/pub/week48/html/._week48-bs053.html b/doc/pub/week48/html/._week48-bs053.html index b776a6ef0..a9b3de55e 100644 --- a/doc/pub/week48/html/._week48-bs053.html +++ b/doc/pub/week48/html/._week48-bs053.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    7. Contents
                      8. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Dual learning

                      +

                      Dual learning

                      Dual learning is a new learning paradigm, the basic idea of which is diff --git a/doc/pub/week48/html/._week48-bs054.html b/doc/pub/week48/html/._week48-bs054.html index 4afce6510..13c664bb6 100644 --- a/doc/pub/week48/html/._week48-bs054.html +++ b/doc/pub/week48/html/._week48-bs054.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    8. Contents
                      9. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Distributed machine learning

                      +

                      Distributed machine learning

                      Distributed computation will speed up machine learning algorithms, diff --git a/doc/pub/week48/html/._week48-bs055.html b/doc/pub/week48/html/._week48-bs055.html index f567f23b6..1d07e5f4b 100644 --- a/doc/pub/week48/html/._week48-bs055.html +++ b/doc/pub/week48/html/._week48-bs055.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    9. Contents
                      10. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Meta learning

                      +

                      Meta learning

                      Meta learning is an emerging research direction in machine diff --git a/doc/pub/week48/html/._week48-bs056.html b/doc/pub/week48/html/._week48-bs056.html index e84530d11..042426255 100644 --- a/doc/pub/week48/html/._week48-bs056.html +++ b/doc/pub/week48/html/._week48-bs056.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    10. Contents
                      11. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      The Challenges Facing Machine Learning

                      +

                      The Challenges Facing Machine Learning

                      While there has been much progress in machine learning, there are also challenges. diff --git a/doc/pub/week48/html/._week48-bs057.html b/doc/pub/week48/html/._week48-bs057.html index 319f7a31a..784ec6145 100644 --- a/doc/pub/week48/html/._week48-bs057.html +++ b/doc/pub/week48/html/._week48-bs057.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    11. Contents
                      12. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Explainable machine learning

                      +

                      Explainable machine learning

                      Machine learning, especially deep learning, evolves rapidly. The diff --git a/doc/pub/week48/html/._week48-bs058.html b/doc/pub/week48/html/._week48-bs058.html index 1210d4af3..9ffbac93c 100644 --- a/doc/pub/week48/html/._week48-bs058.html +++ b/doc/pub/week48/html/._week48-bs058.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    12. Contents
                      13. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Quantum machine learning

                      +

                      Quantum machine learning

                      Quantum machine learning is an emerging interdisciplinary research diff --git a/doc/pub/week48/html/._week48-bs059.html b/doc/pub/week48/html/._week48-bs059.html index 3ec7a53c2..3304cec8f 100644 --- a/doc/pub/week48/html/._week48-bs059.html +++ b/doc/pub/week48/html/._week48-bs059.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    13. Contents
                      14. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Quantum machine learning algorithms based on linear algebra

                      +

                      Quantum machine learning algorithms based on linear algebra

                      Many quantum machine learning algorithms are based on variants of diff --git a/doc/pub/week48/html/._week48-bs060.html b/doc/pub/week48/html/._week48-bs060.html index 36fa524fd..1fa2343d8 100644 --- a/doc/pub/week48/html/._week48-bs060.html +++ b/doc/pub/week48/html/._week48-bs060.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    14. Contents
                      15. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Quantum reinforcement learning

                      +

                      Quantum reinforcement learning

                      In quantum reinforcement learning, a quantum agent interacts with the diff --git a/doc/pub/week48/html/._week48-bs061.html b/doc/pub/week48/html/._week48-bs061.html index 8c3b63ed6..44c25d4e5 100644 --- a/doc/pub/week48/html/._week48-bs061.html +++ b/doc/pub/week48/html/._week48-bs061.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    15. Contents
                      16. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Quantum deep learning

                      +

                      Quantum deep learning

                      Dedicated quantum information processors, such as quantum annealers diff --git a/doc/pub/week48/html/._week48-bs062.html b/doc/pub/week48/html/._week48-bs062.html index f6845296f..14420f92a 100644 --- a/doc/pub/week48/html/._week48-bs062.html +++ b/doc/pub/week48/html/._week48-bs062.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    16. Contents
                      17. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Social machine learning

                      +

                      Social machine learning

                      Machine learning aims to imitate how humans diff --git a/doc/pub/week48/html/._week48-bs063.html b/doc/pub/week48/html/._week48-bs063.html index 1d6d7d14e..705392c3d 100644 --- a/doc/pub/week48/html/._week48-bs063.html +++ b/doc/pub/week48/html/._week48-bs063.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    17. Contents
                      18. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      The last words?

                      +

                      The last words?

                      Early computer scientist Alan Kay said, The best way to predict the diff --git a/doc/pub/week48/html/._week48-bs064.html b/doc/pub/week48/html/._week48-bs064.html index c8dea4cd3..f9a3273f6 100644 --- a/doc/pub/week48/html/._week48-bs064.html +++ b/doc/pub/week48/html/._week48-bs064.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({

                    18. Contents
                      19. @@ -265,7 +263,7 @@ MathJax.Hub.Config({ -

                      Best wishes to you all and thanks so much for your heroic efforts this semester

                      +

                      Best wishes to you all and thanks so much for your heroic efforts this semester





                      diff --git a/doc/pub/week48/html/week48-bs.html b/doc/pub/week48/html/week48-bs.html index 20bc14f85..36457c41d 100644 --- a/doc/pub/week48/html/week48-bs.html +++ b/doc/pub/week48/html/week48-bs.html @@ -46,108 +46,107 @@ Automatically generated HTML file from DocOnce source ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -185,71 +184,70 @@ MathJax.Hub.Config({
                    19. Contents
                      20. diff --git a/doc/pub/week48/html/week48-reveal.html b/doc/pub/week48/html/week48-reveal.html index ebd4d8082..294b51082 100644 --- a/doc/pub/week48/html/week48-reveal.html +++ b/doc/pub/week48/html/week48-reveal.html @@ -229,20 +229,21 @@ wavelets, splines etc.

                      If our feature space is not easy to separate, as shown in the figure -<<<<<<< HEAD -here, we can achieve a better separation by introducing more complex -basis functions. The ideal would be, as shown in the next figure, to, +generated by the code below, we can achieve a better separation by introducing a more complex +basis functions. The ideal would be, as shown by the code example below, to, via a specific transformation to obtain a separation between the -classes which is almost linear. -====

                      === -here generated by the code below (see also Figures 12.2 and 12.3 of Hastie et al.), we can achieve a better separation by introducing more complex -basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to -obtain a separation between the classes which is almost linear. ->>>>>>> origin/master +classes that is almost linear. See also Figures 12.2 and 12.3 of Hastie et al..

                      -The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same type of equations to be solved, except that -we need to introduce, for example, a polynomial transformation to a two-dimensional training set. +We can achieve a better separation by introducing more complex +basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to +obtain a separation between the classes which is almost linear. + +

                      +The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same +type of equations to be solved, except that we need to introduce, for +example, a polynomial transformation to a two-dimensional training +set.

                      @@ -298,7 +299,7 @@ plt.show()

                      -

                      The equations

                      +

                      The equations

                      Suppose we define a polynomial transformation of degree two only. We define a vector \( \boldsymbol{x}_i=[x_i,y_i] \) and have @@ -356,7 +357,7 @@ kernel trick.

                      -

                      The problem to solve

                      +

                      The problem to solve

                      Using our definition of the kernel, we can rewrite again the Lagrangian @@ -385,7 +386,7 @@ If we add the slack constants this leads to the additional constraint \( 0\leq \

                      -

                      Tailoring the equations to the usage of CVXOPT

                      +

                      Tailoring the equations to the usage of CVXOPT

                      We can rewrite this (see the solutions below) in terms of a convex optimization problem of the type @@ -405,7 +406,7 @@ Given a kernel \( K \) and the targets \( y_i \) this matrix is easy to set up.

                      -

                      Different kernels and Mercer's theorem

                      +

                      Different kernels and Mercer's theorem

                      There are several popular kernels being used. These are @@ -446,7 +447,7 @@ in practice.

                      -

                      The moons example (Adapted from Geron, chapter 5)

                      +

                      The moons example (Adapted from Geron, chapter 5)

                      @@ -643,7 +644,7 @@ plt.show()

                      -

                      Mathematical optimization of convex functions

                      +

                      Mathematical optimization of convex functions

                      A mathematical (quadratic) optimization problem, or just optimization problem, has the form @@ -670,7 +671,7 @@ Convex optimization problems play a central role in applied mathematics and we r

                      -

                      How do we solve these problems?

                      +

                      How do we solve these problems?

                      If we use Python as programming language and wish to venture beyond @@ -696,7 +697,7 @@ This will make our life much easier. You don't need to write your own optimizer.

                      -

                      A simple example

                      +

                      A simple example

                      We remind ourselves about the general problem we want to solve @@ -777,7 +778,7 @@ sol['primal objective']

                      -

                      Back to the more realistic cases

                      +

                      Back to the more realistic cases

                      We are now ready to return to our setup of the optmization problem for a more realistic case. Introducing the slack parameter \( C \) we have @@ -799,7 +800,7 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb

                      -

                      Setting up the matrices and the problem

                      +

                      Setting up the matrices and the problem

                      We have the general problem @@ -824,10 +825,10 @@ $$

                      -

                      Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

                      +

                      Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

                      -We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the contraint +We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the constraint \( \ge \) with \( -1 \) in order to get \( \le \). It means that the problem \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \) can be written as

                       
                      @@ -848,7 +849,7 @@ $$ \lambda_3 \\ \dots \\ \lambda_n \\ -\end{bmatrix}\wedge +\end{bmatrix} \preceq \begin{bmatrix} 0 \\ 0 \\ 0 \\ @@ -869,7 +870,7 @@ And then we are ready to go.

                      -

                      SVMs and Regression and multiclass classification

                      +

                      SVMs and Regression and multiclass classification

                      More text will be added here. See also Hastie et al. section 12.3. @@ -877,18 +878,18 @@ More text will be added here. See also Summary of course +

                      Summary of course

                      -

                      What? Me worry? No final exam in this course!

                      +

                      What? Me worry? No final exam in this course!





                      -

                      Topics we have covered this year

                      +

                      Topics we have covered this year

                      The course has two central parts @@ -901,7 +902,7 @@ The course has two central parts

                      -

                      Statistical analysis and optimization of data

                      +

                      Statistical analysis and optimization of data

                      The following topics have been discussed: @@ -919,7 +920,7 @@ The following topics have been discussed:

                      -

                      Machine learning

                      +

                      Machine learning

                      The following topics will be covered @@ -961,7 +962,7 @@ The following topics will be covered

                      -

                      Learning outcomes and overarching aims of this course

                      +

                      Learning outcomes and overarching aims of this course

                      The course introduces a variety of central algorithms and methods @@ -988,7 +989,7 @@ ethical conduct is emphasized throughout the course.

                      -

                      Perspective on Machine Learning

                      +

                      Perspective on Machine Learning

                      1. Rapidly emerging application area
                        2. @@ -1006,7 +1007,7 @@ Neural Networks, etc.
                        -

                        Machine Learning Research

                        +

                        Machine Learning Research

                        Where to find recent results: @@ -1023,7 +1024,7 @@ Where to find recent results:

                        -

                        Starting your Machine Learning Project

                        +

                        Starting your Machine Learning Project

                        1. Identify problem type: classification, regression
                          2. @@ -1036,7 +1037,7 @@ Where to find recent results:
                          -

                          Choose a Model and Algorithm

                          +

                          Choose a Model and Algorithm

                          1. Supervised?
                            2. @@ -1047,7 +1048,7 @@ Where to find recent results:
                            -

                            Preparing Your Data

                            +

                            Preparing Your Data

                            1. Shuffle your data
                              2. @@ -1077,7 +1078,7 @@ Where to find recent results:
                              -

                              Which Activation and Weights to Choose in Neural Networks

                              +

                              Which Activation and Weights to Choose in Neural Networks

                              1. RELU? ELU?
                                2. @@ -1100,7 +1101,7 @@ Where to find recent results:
                                -

                                Optimization Methods and Hyperparameters

                                +

                                Optimization Methods and Hyperparameters

                                1. Stochastic gradient descent @@ -1129,7 +1130,7 @@ set of hyperparameters and regularization methods.
                                  -

                                  Resampling

                                  +

                                  Resampling

                                  When do we resample? @@ -1143,7 +1144,7 @@ When do we resample?

                                  -

                                  Other courses on Data science and Machine Learning at UiO

                                  +

                                  Other courses on Data science and Machine Learning at UiO

                                  The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. @@ -1162,7 +1163,7 @@ The link here Additional courses of interest +

                                  Additional courses of interest

                                  1. STK4051 Computational Statistics
                                    2. @@ -1172,7 +1173,7 @@ The link here What's the future like? +

                                    What's the future like?

                                    Based on multi-layer nonlinear neural networks, deep learning can @@ -1196,7 +1197,7 @@ networks have been proposed, such as

                                    -

                                    Types of Machine Learning, a repetition

                                    +

                                    Types of Machine Learning, a repetition

                                    @@ -1229,7 +1230,7 @@ Some of the most common tasks are:
                                    -

                                    Why Boltzmann machines?

                                    +

                                    Why Boltzmann machines?

                                    What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. @@ -1244,7 +1245,7 @@ Furthermore, they have been used to solve complicated Boltzmann Machines +

                                    Boltzmann Machines

                                    Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? @@ -1264,7 +1265,7 @@ Why use a generative model rather than the more well known discriminative deep n

                                    -

                                    Some similarities and differences from DNNs

                                    +

                                    Some similarities and differences from DNNs

                                    1. Both use gradient-descent based learning procedures for minimizing cost functions
                                      2. @@ -1278,7 +1279,7 @@ History: The RBM was developed by amongst others                                       Boltzmann machines (BM) +

                                      Boltzmann machines (BM)

                                      @@ -1309,7 +1310,7 @@ the learned distribution.
                                      -

                                      A standard BM setup

                                      +

                                      A standard BM setup

                                      @@ -1339,7 +1340,7 @@ Here we take away all lateral connections between nodes in the visible layer as
                                      -

                                      The structure of the RBM network

                                      +

                                      The structure of the RBM network





                                      @@ -1347,7 +1348,7 @@ Here we take away all lateral connections between nodes in the visible layer as
                                      -

                                      The network

                                      +

                                      The network

                                      The network layers: @@ -1360,7 +1361,7 @@ Here we take away all lateral connections between nodes in the visible layer as

                                      -

                                      Goals

                                      +

                                      Goals

                                      The goal of the hidden layer is to increase the model's expressive @@ -1382,7 +1383,7 @@ over (integrated out).

                                      -

                                      Joint distribution

                                      +

                                      Joint distribution

                                      The restricted Boltzmann machine is described by a Boltzmann distribution @@ -1410,7 +1411,7 @@ It is common to ignore \( T_0 \) by setting it to one.

                                      -

                                      Network Elements, the energy function

                                      +

                                      Network Elements, the energy function

                                      The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a @@ -1440,7 +1441,7 @@ The connection between the nodes in the two layers is given by the weights \( w_

                                      -

                                      Defining different types of RBMs

                                      +

                                      Defining different types of RBMs

                                      There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \).

                                      @@ -1477,7 +1478,7 @@ $$

                                      -

                                      More about RBMs

                                      +

                                      More about RBMs

                                      1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
                                        2. @@ -1500,7 +1501,7 @@ To read more, see Autoencoders: Overarching view +

                                        Autoencoders: Overarching view

                                        Autoencoders are artificial neural networks capable of learning @@ -1538,7 +1539,7 @@ See also A. Geron's textbook, chapter 15.

                                        -

                                        Bayesian Machine Learning

                                        +

                                        Bayesian Machine Learning

                                        This is an important topic if we aim at extracting a probability @@ -1560,7 +1561,7 @@ See also the Reinforcement Learning +

                                        Reinforcement Learning

                                        Reinforcement Learning (RL) is one of the most exciting fields of @@ -1596,7 +1597,7 @@ See also A. Geron's textbook, chapter 16.

                                        -

                                        Transfer learning

                                        +

                                        Transfer learning

                                        The goal of transfer learning is to transfer the model or knowledge @@ -1614,7 +1615,7 @@ research topic in recent years, with many problems still waiting to be studied.

                                        -

                                        Adversarial learning

                                        +

                                        Adversarial learning

                                        The conventional deep generative model has a potential problem: the @@ -1633,7 +1634,7 @@ successfully applied to image, speech, and text.

                                        -

                                        Dual learning

                                        +

                                        Dual learning

                                        Dual learning is a new learning paradigm, the basic idea of which is @@ -1649,7 +1650,7 @@ image-to-text, and text-to-image.

                                        -

                                        Distributed machine learning

                                        +

                                        Distributed machine learning

                                        Distributed computation will speed up machine learning algorithms, @@ -1660,7 +1661,7 @@ implementing the machine learning algorithms in parallel is required.

                                        -

                                        Meta learning

                                        +

                                        Meta learning

                                        Meta learning is an emerging research direction in machine @@ -1674,7 +1675,7 @@ tasks.

                                        -

                                        The Challenges Facing Machine Learning

                                        +

                                        The Challenges Facing Machine Learning

                                        While there has been much progress in machine learning, there are also challenges. @@ -1702,7 +1703,7 @@ See the article on Explainable machine learning +

                                        Explainable machine learning

                                        Machine learning, especially deep learning, evolves rapidly. The @@ -1730,7 +1731,7 @@ problems by logical reasoning.

                                        -

                                        Quantum machine learning

                                        +

                                        Quantum machine learning

                                        Quantum machine learning is an emerging interdisciplinary research @@ -1761,7 +1762,7 @@ computing systems.

                                        -

                                        Quantum machine learning algorithms based on linear algebra

                                        +

                                        Quantum machine learning algorithms based on linear algebra

                                        Many quantum machine learning algorithms are based on variants of @@ -1780,7 +1781,7 @@ input data into a quantum system is as yet unknown for most cases.

                                        -

                                        Quantum reinforcement learning

                                        +

                                        Quantum reinforcement learning

                                        In quantum reinforcement learning, a quantum agent interacts with the @@ -1794,7 +1795,7 @@ superconducting circuits and systems of trapped ions.

                                        -

                                        Quantum deep learning

                                        +

                                        Quantum deep learning

                                        Dedicated quantum information processors, such as quantum annealers @@ -1813,7 +1814,7 @@ result.

                                        -

                                        Social machine learning

                                        +

                                        Social machine learning

                                        Machine learning aims to imitate how humans @@ -1831,7 +1832,7 @@ And much more.

                                        -

                                        The last words?

                                        +

                                        The last words?

                                        Early computer scientist Alan Kay said, The best way to predict the @@ -1843,7 +1844,7 @@ topics. Together, we will not just predict the future, but create it.

                                        -

                                        Best wishes to you all and thanks so much for your heroic efforts this semester

                                        +

                                        Best wishes to you all and thanks so much for your heroic efforts this semester





                                        diff --git a/doc/pub/week48/html/week48-solarized.html b/doc/pub/week48/html/week48-solarized.html index 758b74f77..91d65d76d 100644 --- a/doc/pub/week48/html/week48-solarized.html +++ b/doc/pub/week48/html/week48-solarized.html @@ -66,108 +66,107 @@ div { text-align: justify; text-justify: inter-word; } ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -283,20 +282,21 @@ wavelets, splines etc.

                                        If our feature space is not easy to separate, as shown in the figure -<<<<<<< HEAD -here, we can achieve a better separation by introducing more complex -basis functions. The ideal would be, as shown in the next figure, to, +generated by the code below, we can achieve a better separation by introducing a more complex +basis functions. The ideal would be, as shown by the code example below, to, via a specific transformation to obtain a separation between the -classes which is almost linear. -====

                                        === -here generated by the code below (see also Figures 12.2 and 12.3 of                                         Hastie et al.), we can achieve a better separation by introducing more complex -basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to -obtain a separation between the classes which is almost linear. ->>>>>>> origin/master +classes that is almost linear. See also Figures 12.2 and 12.3 of Hastie et al..

                                        -The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same type of equations to be solved, except that -we need to introduce, for example, a polynomial transformation to a two-dimensional training set. +We can achieve a better separation by introducing more complex +basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to +obtain a separation between the classes which is almost linear. + +

                                        +The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same +type of equations to be solved, except that we need to introduce, for +example, a polynomial transformation to a two-dimensional training +set.

                                        @@ -351,7 +351,7 @@ plt.show()











                                        -

                                        The equations

                                        +

                                        The equations

                                        Suppose we define a polynomial transformation of degree two only. We define a vector \( \boldsymbol{x}_i=[x_i,y_i] \) and have @@ -397,7 +397,7 @@ kernel trick.











                                        -

                                        The problem to solve

                                        +

                                        The problem to solve

                                        Using our definition of the kernel, we can rewrite again the Lagrangian @@ -422,7 +422,7 @@ If we add the slack constants this leads to the additional constraint \( 0\leq \











                                        -

                                        Tailoring the equations to the usage of CVXOPT

                                        +

                                        Tailoring the equations to the usage of CVXOPT

                                        We can rewrite this (see the solutions below) in terms of a convex optimization problem of the type @@ -440,7 +440,7 @@ Given a kernel \( K \) and the targets \( y_i \) this matrix is easy to set up.











                                        -

                                        Different kernels and Mercer's theorem

                                        +

                                        Different kernels and Mercer's theorem

                                        There are several popular kernels being used. These are @@ -478,7 +478,7 @@ in practice.











                                        -

                                        The moons example (Adapted from Geron, chapter 5)

                                        +

                                        The moons example (Adapted from Geron, chapter 5)

                                        @@ -674,7 +674,7 @@ plt.show()











                                        -

                                        Mathematical optimization of convex functions

                                        +

                                        Mathematical optimization of convex functions

                                        A mathematical (quadratic) optimization problem, or just optimization problem, has the form @@ -699,7 +699,7 @@ Convex optimization problems play a central role in applied mathematics and we r











                                        -

                                        How do we solve these problems?

                                        +

                                        How do we solve these problems?

                                        If we use Python as programming language and wish to venture beyond @@ -725,7 +725,7 @@ This will make our life much easier. You don't need to write your own optimizer.











                                        -

                                        A simple example

                                        +

                                        A simple example

                                        We remind ourselves about the general problem we want to solve @@ -793,7 +793,7 @@ sol['primal objective']











                                        -

                                        Back to the more realistic cases

                                        +

                                        Back to the more realistic cases

                                        We are now ready to return to our setup of the optmization problem for a more realistic case. Introducing the slack parameter \( C \) we have @@ -813,7 +813,7 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb











                                        -

                                        Setting up the matrices and the problem

                                        +

                                        Setting up the matrices and the problem

                                        We have the general problem @@ -835,10 +835,10 @@ $$









                                        -

                                        Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

                                        +

                                        Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

                                        -We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the contraint +We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the constraint \( \ge \) with \( -1 \) in order to get \( \le \). It means that the problem \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \) can be written as $$ @@ -858,7 +858,7 @@ $$ \lambda_3 \\ \dots \\ \lambda_n \\ -\end{bmatrix}\wedge +\end{bmatrix} \preceq \begin{bmatrix} 0 \\ 0 \\ 0 \\ @@ -878,7 +878,7 @@ And then we are ready to go.











                                        -

                                        SVMs and Regression and multiclass classification

                                        +

                                        SVMs and Regression and multiclass classification

                                        More text will be added here. See also Hastie et al. section 12.3. @@ -886,18 +886,18 @@ More text will be added here. See also Summary of course +

                                        Summary of course











                                        -

                                        What? Me worry? No final exam in this course!

                                        +

                                        What? Me worry? No final exam in this course!















                                        -

                                        Topics we have covered this year

                                        +

                                        Topics we have covered this year

                                        The course has two central parts @@ -909,7 +909,7 @@ The course has two central parts









                                        -

                                        Statistical analysis and optimization of data

                                        +

                                        Statistical analysis and optimization of data

                                        The following topics have been discussed: @@ -926,7 +926,7 @@ The following topics have been discussed:









                                        -

                                        Machine learning

                                        +

                                        Machine learning

                                        The following topics will be covered @@ -970,7 +970,7 @@ The following topics will be covered









                                        -

                                        Learning outcomes and overarching aims of this course

                                        +

                                        Learning outcomes and overarching aims of this course

                                        The course introduces a variety of central algorithms and methods @@ -996,7 +996,7 @@ ethical conduct is emphasized throughout the course.









                                        -

                                        Perspective on Machine Learning

                                        +

                                        Perspective on Machine Learning

                                        1. Rapidly emerging application area
                                          2. @@ -1013,7 +1013,7 @@ Neural Networks, etc.











                                          -

                                          Machine Learning Research

                                          +

                                          Machine Learning Research

                                          Where to find recent results: @@ -1029,7 +1029,7 @@ Where to find recent results:









                                          -

                                          Starting your Machine Learning Project

                                          +

                                          Starting your Machine Learning Project

                                          1. Identify problem type: classification, regression
                                            2. @@ -1041,7 +1041,7 @@ Where to find recent results:









                                            -

                                            Choose a Model and Algorithm

                                            +

                                            Choose a Model and Algorithm

                                            1. Supervised?
                                              2. @@ -1051,7 +1051,7 @@ Where to find recent results:









                                              -

                                              Preparing Your Data

                                              +

                                              Preparing Your Data

                                              1. Shuffle your data
                                                2. @@ -1079,7 +1079,7 @@ Where to find recent results:









                                                -

                                                Which Activation and Weights to Choose in Neural Networks

                                                +

                                                Which Activation and Weights to Choose in Neural Networks

                                                1. RELU? ELU?
                                                  2. @@ -1100,7 +1100,7 @@ Where to find recent results:









                                                  -

                                                  Optimization Methods and Hyperparameters

                                                  +

                                                  Optimization Methods and Hyperparameters

                                                  1. Stochastic gradient descent @@ -1126,7 +1126,7 @@ set of hyperparameters and regularization methods.











                                                    -

                                                    Resampling

                                                    +

                                                    Resampling

                                                    When do we resample? @@ -1139,7 +1139,7 @@ When do we resample?









                                                    -

                                                    Other courses on Data science and Machine Learning at UiO

                                                    +

                                                    Other courses on Data science and Machine Learning at UiO

                                                    The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. @@ -1157,7 +1157,7 @@ The link here Additional courses of interest +

                                                    Additional courses of interest

                                                    1. STK4051 Computational Statistics
                                                      2. @@ -1166,7 +1166,7 @@ The link here What's the future like? +

                                                      What's the future like?

                                                      Based on multi-layer nonlinear neural networks, deep learning can @@ -1189,7 +1189,7 @@ networks have been proposed, such as









                                                      -

                                                      Types of Machine Learning, a repetition

                                                      +

                                                      Types of Machine Learning, a repetition

                                                      @@ -1219,7 +1219,7 @@ Some of the most common tasks are:











                                                      -

                                                      Why Boltzmann machines?

                                                      +

                                                      Why Boltzmann machines?

                                                      What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. @@ -1234,7 +1234,7 @@ Furthermore, they have been used to solve complicated Boltzmann Machines +

                                                      Boltzmann Machines

                                                      Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? @@ -1253,7 +1253,7 @@ Why use a generative model rather than the more well known discriminative deep n









                                                      -

                                                      Some similarities and differences from DNNs

                                                      +

                                                      Some similarities and differences from DNNs

                                                      1. Both use gradient-descent based learning procedures for minimizing cost functions
                                                        2. @@ -1266,7 +1266,7 @@ History: The RBM was developed by amongst others                                                         Boltzmann machines (BM) +

                                                        Boltzmann machines (BM)

                                                        @@ -1298,7 +1298,7 @@ the learned distribution.











                                                        -

                                                        A standard BM setup

                                                        +

                                                        A standard BM setup

                                                        @@ -1331,7 +1331,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                                        -

                                                        The structure of the RBM network

                                                        +

                                                        The structure of the RBM network





                                                        @@ -1339,7 +1339,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                                        -

                                                        The network

                                                        +

                                                        The network

                                                        The network layers: @@ -1351,7 +1351,7 @@ Here we take away all lateral connections between nodes in the visible layer as









                                                        -

                                                        Goals

                                                        +

                                                        Goals

                                                        The goal of the hidden layer is to increase the model's expressive @@ -1372,7 +1372,7 @@ over (integrated out).









                                                        -

                                                        Joint distribution

                                                        +

                                                        Joint distribution

                                                        The restricted Boltzmann machine is described by a Boltzmann distribution @@ -1396,7 +1396,7 @@ It is common to ignore \( T_0 \) by setting it to one.











                                                        -

                                                        Network Elements, the energy function

                                                        +

                                                        Network Elements, the energy function

                                                        The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a @@ -1424,7 +1424,7 @@ The connection between the nodes in the two layers is given by the weights \( w_











                                                        -

                                                        Defining different types of RBMs

                                                        +

                                                        Defining different types of RBMs

                                                        There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \).

                                                        @@ -1462,7 +1462,7 @@ $$











                                                        -

                                                        More about RBMs

                                                        +

                                                        More about RBMs

                                                        1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
                                                          2. @@ -1483,7 +1483,7 @@ To read more, see                                                           Autoencoders: Overarching view +

                                                          Autoencoders: Overarching view

                                                          Autoencoders are artificial neural networks capable of learning @@ -1521,7 +1521,7 @@ See also A. Geron's textbook, chapter 15.











                                                          -

                                                          Bayesian Machine Learning

                                                          +

                                                          Bayesian Machine Learning

                                                          This is an important topic if we aim at extracting a probability @@ -1543,7 +1543,7 @@ See also the Reinforcement Learning +

                                                          Reinforcement Learning

                                                          Reinforcement Learning (RL) is one of the most exciting fields of @@ -1579,7 +1579,7 @@ See also A. Geron's textbook, chapter 16.











                                                          -

                                                          Transfer learning

                                                          +

                                                          Transfer learning

                                                          The goal of transfer learning is to transfer the model or knowledge @@ -1597,7 +1597,7 @@ research topic in recent years, with many problems still waiting to be studied.











                                                          -

                                                          Adversarial learning

                                                          +

                                                          Adversarial learning

                                                          The conventional deep generative model has a potential problem: the @@ -1616,7 +1616,7 @@ successfully applied to image, speech, and text.











                                                          -

                                                          Dual learning

                                                          +

                                                          Dual learning

                                                          Dual learning is a new learning paradigm, the basic idea of which is @@ -1632,7 +1632,7 @@ image-to-text, and text-to-image.











                                                          -

                                                          Distributed machine learning

                                                          +

                                                          Distributed machine learning

                                                          Distributed computation will speed up machine learning algorithms, @@ -1643,7 +1643,7 @@ implementing the machine learning algorithms in parallel is required.











                                                          -

                                                          Meta learning

                                                          +

                                                          Meta learning

                                                          Meta learning is an emerging research direction in machine @@ -1657,7 +1657,7 @@ tasks.











                                                          -

                                                          The Challenges Facing Machine Learning

                                                          +

                                                          The Challenges Facing Machine Learning

                                                          While there has been much progress in machine learning, there are also challenges. @@ -1685,7 +1685,7 @@ See the article on Explainable machine learning +

                                                          Explainable machine learning

                                                          Machine learning, especially deep learning, evolves rapidly. The @@ -1713,7 +1713,7 @@ problems by logical reasoning.











                                                          -

                                                          Quantum machine learning

                                                          +

                                                          Quantum machine learning

                                                          Quantum machine learning is an emerging interdisciplinary research @@ -1744,7 +1744,7 @@ computing systems.











                                                          -

                                                          Quantum machine learning algorithms based on linear algebra

                                                          +

                                                          Quantum machine learning algorithms based on linear algebra

                                                          Many quantum machine learning algorithms are based on variants of @@ -1763,7 +1763,7 @@ input data into a quantum system is as yet unknown for most cases.











                                                          -

                                                          Quantum reinforcement learning

                                                          +

                                                          Quantum reinforcement learning

                                                          In quantum reinforcement learning, a quantum agent interacts with the @@ -1777,7 +1777,7 @@ superconducting circuits and systems of trapped ions.











                                                          -

                                                          Quantum deep learning

                                                          +

                                                          Quantum deep learning

                                                          Dedicated quantum information processors, such as quantum annealers @@ -1796,7 +1796,7 @@ result.











                                                          -

                                                          Social machine learning

                                                          +

                                                          Social machine learning

                                                          Machine learning aims to imitate how humans @@ -1814,7 +1814,7 @@ And much more.











                                                          -

                                                          The last words?

                                                          +

                                                          The last words?

                                                          Early computer scientist Alan Kay said, The best way to predict the @@ -1826,7 +1826,7 @@ topics. Together, we will not just predict the future, but create it.











                                                          -

                                                          Best wishes to you all and thanks so much for your heroic efforts this semester

                                                          +

                                                          Best wishes to you all and thanks so much for your heroic efforts this semester





                                                          diff --git a/doc/pub/week48/html/week48.html b/doc/pub/week48/html/week48.html index 626dbde5f..2acca5281 100644 --- a/doc/pub/week48/html/week48.html +++ b/doc/pub/week48/html/week48.html @@ -71,108 +71,107 @@ div { text-align: justify; text-justify: inter-word; } ('Friday', 2, None, '___sec2'), ('Support Vector Machines, overarching aims', 2, None, '___sec3'), ('Kernels and non-linearity', 2, None, '___sec4'), - ('=', 3, None, '___sec5'), - ('The equations', 2, None, '___sec6'), - ('The problem to solve', 2, None, '___sec7'), + ('The equations', 2, None, '___sec5'), + ('The problem to solve', 2, None, '___sec6'), ('Tailoring the equations to the usage of CVXOPT', 2, None, - '___sec8'), - ("Different kernels and Mercer's theorem", 2, None, '___sec9'), + '___sec7'), + ("Different kernels and Mercer's theorem", 2, None, '___sec8'), ('The moons example ("Adapted from Geron, chapter ' '5":"https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/")', 2, None, - '___sec10'), + '___sec9'), ('Mathematical optimization of convex functions', 2, None, - '___sec11'), - ('How do we solve these problems?', 2, None, '___sec12'), - ('A simple example', 2, None, '___sec13'), - ('Back to the more realistic cases', 2, None, '___sec14'), - ('Setting up the matrices and the problem', 2, None, '___sec15'), + '___sec10'), + ('How do we solve these problems?', 2, None, '___sec11'), + ('A simple example', 2, None, '___sec12'), + ('Back to the more realistic cases', 2, None, '___sec13'), + ('Setting up the matrices and the problem', 2, None, '___sec14'), ('Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq ' '\\boldsymbol{h}$', 2, None, - '___sec16'), + '___sec15'), ('SVMs and Regression and multiclass classification', 2, None, - '___sec17'), - ('Summary of course', 2, None, '___sec18'), + '___sec16'), + ('Summary of course', 2, None, '___sec17'), ('What? Me worry? No final exam in this course!', 2, None, - '___sec19'), - ('Topics we have covered this year', 2, None, '___sec20'), + '___sec18'), + ('Topics we have covered this year', 2, None, '___sec19'), ('Statistical analysis and optimization of data', 2, None, - '___sec21'), - ('Machine learning', 2, None, '___sec22'), + '___sec20'), + ('Machine learning', 2, None, '___sec21'), ('Learning outcomes and overarching aims of this course', 2, None, - '___sec23'), - ('Perspective on Machine Learning', 2, None, '___sec24'), - ('Machine Learning Research', 2, None, '___sec25'), - ('Starting your Machine Learning Project', 2, None, '___sec26'), - ('Choose a Model and Algorithm', 2, None, '___sec27'), - ('Preparing Your Data', 2, None, '___sec28'), + '___sec22'), + ('Perspective on Machine Learning', 2, None, '___sec23'), + ('Machine Learning Research', 2, None, '___sec24'), + ('Starting your Machine Learning Project', 2, None, '___sec25'), + ('Choose a Model and Algorithm', 2, None, '___sec26'), + ('Preparing Your Data', 2, None, '___sec27'), ('Which Activation and Weights to Choose in Neural Networks', 2, None, - '___sec29'), - ('Optimization Methods and Hyperparameters', 2, None, '___sec30'), - ('Resampling', 2, None, '___sec31'), + '___sec28'), + ('Optimization Methods and Hyperparameters', 2, None, '___sec29'), + ('Resampling', 2, None, '___sec30'), ('Other courses on Data science and Machine Learning at UiO', 2, None, - '___sec32'), - ('Additional courses of interest', 2, None, '___sec33'), - ("What's the future like?", 2, None, '___sec34'), - ('Types of Machine Learning, a repetition', 2, None, '___sec35'), - ('Why Boltzmann machines?', 2, None, '___sec36'), - ('Boltzmann Machines', 2, None, '___sec37'), + '___sec31'), + ('Additional courses of interest', 2, None, '___sec32'), + ("What's the future like?", 2, None, '___sec33'), + ('Types of Machine Learning, a repetition', 2, None, '___sec34'), + ('Why Boltzmann machines?', 2, None, '___sec35'), + ('Boltzmann Machines', 2, None, '___sec36'), ('Some similarities and differences from DNNs', 2, None, - '___sec38'), - ('Boltzmann machines (BM)', 2, None, '___sec39'), - ('A standard BM setup', 2, None, '___sec40'), - ('The structure of the RBM network', 2, None, '___sec41'), - ('The network', 2, None, '___sec42'), - ('Goals', 2, None, '___sec43'), - ('Joint distribution', 2, None, '___sec44'), - ('Network Elements, the energy function', 2, None, '___sec45'), - ('Defining different types of RBMs', 2, None, '___sec46'), - ('More about RBMs', 2, None, '___sec47'), - ('Autoencoders: Overarching view', 2, None, '___sec48'), - ('Bayesian Machine Learning', 2, None, '___sec49'), - ('Reinforcement Learning', 2, None, '___sec50'), - ('Transfer learning', 2, None, '___sec51'), - ('Adversarial learning', 2, None, '___sec52'), - ('Dual learning', 2, None, '___sec53'), - ('Distributed machine learning', 2, None, '___sec54'), - ('Meta learning', 2, None, '___sec55'), - ('The Challenges Facing Machine Learning', 2, None, '___sec56'), - ('Explainable machine learning', 2, None, '___sec57'), - ('Quantum machine learning', 2, None, '___sec58'), + '___sec37'), + ('Boltzmann machines (BM)', 2, None, '___sec38'), + ('A standard BM setup', 2, None, '___sec39'), + ('The structure of the RBM network', 2, None, '___sec40'), + ('The network', 2, None, '___sec41'), + ('Goals', 2, None, '___sec42'), + ('Joint distribution', 2, None, '___sec43'), + ('Network Elements, the energy function', 2, None, '___sec44'), + ('Defining different types of RBMs', 2, None, '___sec45'), + ('More about RBMs', 2, None, '___sec46'), + ('Autoencoders: Overarching view', 2, None, '___sec47'), + ('Bayesian Machine Learning', 2, None, '___sec48'), + ('Reinforcement Learning', 2, None, '___sec49'), + ('Transfer learning', 2, None, '___sec50'), + ('Adversarial learning', 2, None, '___sec51'), + ('Dual learning', 2, None, '___sec52'), + ('Distributed machine learning', 2, None, '___sec53'), + ('Meta learning', 2, None, '___sec54'), + ('The Challenges Facing Machine Learning', 2, None, '___sec55'), + ('Explainable machine learning', 2, None, '___sec56'), + ('Quantum machine learning', 2, None, '___sec57'), ('Quantum machine learning algorithms based on linear algebra', 2, None, - '___sec59'), - ('Quantum reinforcement learning', 2, None, '___sec60'), - ('Quantum deep learning', 2, None, '___sec61'), - ('Social machine learning', 2, None, '___sec62'), - ('The last words?', 2, None, '___sec63'), + '___sec58'), + ('Quantum reinforcement learning', 2, None, '___sec59'), + ('Quantum deep learning', 2, None, '___sec60'), + ('Social machine learning', 2, None, '___sec61'), + ('The last words?', 2, None, '___sec62'), ('Best wishes to you all and thanks so much for your heroic ' 'efforts this semester', 2, None, - '___sec64')]} + '___sec63')]} end of tocinfo --> @@ -288,20 +287,21 @@ wavelets, splines etc.

                                                          If our feature space is not easy to separate, as shown in the figure -<<<<<<< HEAD -here, we can achieve a better separation by introducing more complex -basis functions. The ideal would be, as shown in the next figure, to, +generated by the code below, we can achieve a better separation by introducing a more complex +basis functions. The ideal would be, as shown by the code example below, to, via a specific transformation to obtain a separation between the -classes which is almost linear. -====

                                                          === -here generated by the code below (see also Figures 12.2 and 12.3 of                                                           Hastie et al.), we can achieve a better separation by introducing more complex -basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to -obtain a separation between the classes which is almost linear. ->>>>>>> origin/master +classes that is almost linear. See also Figures 12.2 and 12.3 of Hastie et al..

                                                          -The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same type of equations to be solved, except that -we need to introduce, for example, a polynomial transformation to a two-dimensional training set. +We can achieve a better separation by introducing more complex +basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to +obtain a separation between the classes which is almost linear. + +

                                                          +The change of basis, from \( x\rightarrow z=\phi(x) \) leads to the same +type of equations to be solved, except that we need to introduce, for +example, a polynomial transformation to a two-dimensional training +set.

                                                          @@ -356,7 +356,7 @@ plt.show()











                                                          -

                                                          The equations

                                                          +

                                                          The equations

                                                          Suppose we define a polynomial transformation of degree two only. We define a vector \( \boldsymbol{x}_i=[x_i,y_i] \) and have @@ -402,7 +402,7 @@ kernel trick.











                                                          -

                                                          The problem to solve

                                                          +

                                                          The problem to solve

                                                          Using our definition of the kernel, we can rewrite again the Lagrangian @@ -427,7 +427,7 @@ If we add the slack constants this leads to the additional constraint \( 0\leq \











                                                          -

                                                          Tailoring the equations to the usage of CVXOPT

                                                          +

                                                          Tailoring the equations to the usage of CVXOPT

                                                          We can rewrite this (see the solutions below) in terms of a convex optimization problem of the type @@ -445,7 +445,7 @@ Given a kernel \( K \) and the targets \( y_i \) this matrix is easy to set up.











                                                          -

                                                          Different kernels and Mercer's theorem

                                                          +

                                                          Different kernels and Mercer's theorem

                                                          There are several popular kernels being used. These are @@ -483,7 +483,7 @@ in practice.











                                                          -

                                                          The moons example (Adapted from Geron, chapter 5)

                                                          +

                                                          The moons example (Adapted from Geron, chapter 5)

                                                          @@ -679,7 +679,7 @@ plt.show()











                                                          -

                                                          Mathematical optimization of convex functions

                                                          +

                                                          Mathematical optimization of convex functions

                                                          A mathematical (quadratic) optimization problem, or just optimization problem, has the form @@ -704,7 +704,7 @@ Convex optimization problems play a central role in applied mathematics and we r











                                                          -

                                                          How do we solve these problems?

                                                          +

                                                          How do we solve these problems?

                                                          If we use Python as programming language and wish to venture beyond @@ -730,7 +730,7 @@ This will make our life much easier. You don't need to write your own optimizer.











                                                          -

                                                          A simple example

                                                          +

                                                          A simple example

                                                          We remind ourselves about the general problem we want to solve @@ -798,7 +798,7 @@ sol['primal objective']











                                                          -

                                                          Back to the more realistic cases

                                                          +

                                                          Back to the more realistic cases

                                                          We are now ready to return to our setup of the optmization problem for a more realistic case. Introducing the slack parameter \( C \) we have @@ -818,7 +818,7 @@ With the slack constants this leads to the additional constraint \( 0\leq \lamb











                                                          -

                                                          Setting up the matrices and the problem

                                                          +

                                                          Setting up the matrices and the problem

                                                          We have the general problem @@ -840,10 +840,10 @@ $$









                                                          -

                                                          Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

                                                          +

                                                          Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)

                                                          -We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the contraint +We have two constraints, \( 0\le \lambda_i \) and \( \lambda_i \le C \). To do this we multiply the ones with the constraint \( \ge \) with \( -1 \) in order to get \( \le \). It means that the problem \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \) can be written as $$ @@ -863,7 +863,7 @@ $$ \lambda_3 \\ \dots \\ \lambda_n \\ -\end{bmatrix}\wedge +\end{bmatrix} \preceq \begin{bmatrix} 0 \\ 0 \\ 0 \\ @@ -883,7 +883,7 @@ And then we are ready to go.











                                                          -

                                                          SVMs and Regression and multiclass classification

                                                          +

                                                          SVMs and Regression and multiclass classification

                                                          More text will be added here. See also Hastie et al. section 12.3. @@ -891,18 +891,18 @@ More text will be added here. See also Summary of course +

                                                          Summary of course











                                                          -

                                                          What? Me worry? No final exam in this course!

                                                          +

                                                          What? Me worry? No final exam in this course!















                                                          -

                                                          Topics we have covered this year

                                                          +

                                                          Topics we have covered this year

                                                          The course has two central parts @@ -914,7 +914,7 @@ The course has two central parts









                                                          -

                                                          Statistical analysis and optimization of data

                                                          +

                                                          Statistical analysis and optimization of data

                                                          The following topics have been discussed: @@ -931,7 +931,7 @@ The following topics have been discussed:









                                                          -

                                                          Machine learning

                                                          +

                                                          Machine learning

                                                          The following topics will be covered @@ -975,7 +975,7 @@ The following topics will be covered









                                                          -

                                                          Learning outcomes and overarching aims of this course

                                                          +

                                                          Learning outcomes and overarching aims of this course

                                                          The course introduces a variety of central algorithms and methods @@ -1001,7 +1001,7 @@ ethical conduct is emphasized throughout the course.









                                                          -

                                                          Perspective on Machine Learning

                                                          +

                                                          Perspective on Machine Learning

                                                          1. Rapidly emerging application area
                                                            2. @@ -1018,7 +1018,7 @@ Neural Networks, etc.











                                                            -

                                                            Machine Learning Research

                                                            +

                                                            Machine Learning Research

                                                            Where to find recent results: @@ -1034,7 +1034,7 @@ Where to find recent results:









                                                            -

                                                            Starting your Machine Learning Project

                                                            +

                                                            Starting your Machine Learning Project

                                                            1. Identify problem type: classification, regression
                                                              2. @@ -1046,7 +1046,7 @@ Where to find recent results:









                                                              -

                                                              Choose a Model and Algorithm

                                                              +

                                                              Choose a Model and Algorithm

                                                              1. Supervised?
                                                                2. @@ -1056,7 +1056,7 @@ Where to find recent results:









                                                                -

                                                                Preparing Your Data

                                                                +

                                                                Preparing Your Data

                                                                1. Shuffle your data
                                                                  2. @@ -1084,7 +1084,7 @@ Where to find recent results:









                                                                  -

                                                                  Which Activation and Weights to Choose in Neural Networks

                                                                  +

                                                                  Which Activation and Weights to Choose in Neural Networks

                                                                  1. RELU? ELU?
                                                                    2. @@ -1105,7 +1105,7 @@ Where to find recent results:









                                                                    -

                                                                    Optimization Methods and Hyperparameters

                                                                    +

                                                                    Optimization Methods and Hyperparameters

                                                                    1. Stochastic gradient descent @@ -1131,7 +1131,7 @@ set of hyperparameters and regularization methods.











                                                                      -

                                                                      Resampling

                                                                      +

                                                                      Resampling

                                                                      When do we resample? @@ -1144,7 +1144,7 @@ When do we resample?









                                                                      -

                                                                      Other courses on Data science and Machine Learning at UiO

                                                                      +

                                                                      Other courses on Data science and Machine Learning at UiO

                                                                      The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO. @@ -1162,7 +1162,7 @@ The link here Additional courses of interest +

                                                                      Additional courses of interest

                                                                      1. STK4051 Computational Statistics
                                                                        2. @@ -1171,7 +1171,7 @@ The link here What's the future like? +

                                                                        What's the future like?

                                                                        Based on multi-layer nonlinear neural networks, deep learning can @@ -1194,7 +1194,7 @@ networks have been proposed, such as









                                                                        -

                                                                        Types of Machine Learning, a repetition

                                                                        +

                                                                        Types of Machine Learning, a repetition

                                                                        @@ -1224,7 +1224,7 @@ Some of the most common tasks are:











                                                                        -

                                                                        Why Boltzmann machines?

                                                                        +

                                                                        Why Boltzmann machines?

                                                                        What is known as restricted Boltzmann Machines (RMB) have received a lot of attention lately. @@ -1239,7 +1239,7 @@ Furthermore, they have been used to solve complicated Boltzmann Machines +

                                                                        Boltzmann Machines

                                                                        Why use a generative model rather than the more well known discriminative deep neural networks (DNN)? @@ -1258,7 +1258,7 @@ Why use a generative model rather than the more well known discriminative deep n









                                                                        -

                                                                        Some similarities and differences from DNNs

                                                                        +

                                                                        Some similarities and differences from DNNs

                                                                        1. Both use gradient-descent based learning procedures for minimizing cost functions
                                                                          2. @@ -1271,7 +1271,7 @@ History: The RBM was developed by amongst others                                                                           Boltzmann machines (BM) +

                                                                          Boltzmann machines (BM)

                                                                          @@ -1303,7 +1303,7 @@ the learned distribution.











                                                                          -

                                                                          A standard BM setup

                                                                          +

                                                                          A standard BM setup

                                                                          @@ -1336,7 +1336,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                                                          -

                                                                          The structure of the RBM network

                                                                          +

                                                                          The structure of the RBM network





                                                                          @@ -1344,7 +1344,7 @@ Here we take away all lateral connections between nodes in the visible layer as











                                                                          -

                                                                          The network

                                                                          +

                                                                          The network

                                                                          The network layers: @@ -1356,7 +1356,7 @@ Here we take away all lateral connections between nodes in the visible layer as









                                                                          -

                                                                          Goals

                                                                          +

                                                                          Goals

                                                                          The goal of the hidden layer is to increase the model's expressive @@ -1377,7 +1377,7 @@ over (integrated out).









                                                                          -

                                                                          Joint distribution

                                                                          +

                                                                          Joint distribution

                                                                          The restricted Boltzmann machine is described by a Boltzmann distribution @@ -1401,7 +1401,7 @@ It is common to ignore \( T_0 \) by setting it to one.











                                                                          -

                                                                          Network Elements, the energy function

                                                                          +

                                                                          Network Elements, the energy function

                                                                          The function \( E(\mathbf{x},\mathbf{h}) \) gives the energy of a @@ -1429,7 +1429,7 @@ The connection between the nodes in the two layers is given by the weights \( w_











                                                                          -

                                                                          Defining different types of RBMs

                                                                          +

                                                                          Defining different types of RBMs

                                                                          There are different variants of RBMs, and the differences lie in the types of visible and hidden units we choose as well as in the implementation of the energy function \( E(\mathbf{x},\mathbf{h}) \).

                                                                          @@ -1467,7 +1467,7 @@ $$











                                                                          -

                                                                          More about RBMs

                                                                          +

                                                                          More about RBMs

                                                                          1. Useful when we model continuous data (i.e., we wish \( \mathbf{x} \) to be continuous)
                                                                            2. @@ -1488,7 +1488,7 @@ To read more, see                                                                             Autoencoders: Overarching view +

                                                                            Autoencoders: Overarching view

                                                                            Autoencoders are artificial neural networks capable of learning @@ -1526,7 +1526,7 @@ See also A. Geron's textbook, chapter 15.











                                                                            -

                                                                            Bayesian Machine Learning

                                                                            +

                                                                            Bayesian Machine Learning

                                                                            This is an important topic if we aim at extracting a probability @@ -1548,7 +1548,7 @@ See also the Reinforcement Learning +

                                                                            Reinforcement Learning

                                                                            Reinforcement Learning (RL) is one of the most exciting fields of @@ -1584,7 +1584,7 @@ See also A. Geron's textbook, chapter 16.











                                                                            -

                                                                            Transfer learning

                                                                            +

                                                                            Transfer learning

                                                                            The goal of transfer learning is to transfer the model or knowledge @@ -1602,7 +1602,7 @@ research topic in recent years, with many problems still waiting to be studied.











                                                                            -

                                                                            Adversarial learning

                                                                            +

                                                                            Adversarial learning

                                                                            The conventional deep generative model has a potential problem: the @@ -1621,7 +1621,7 @@ successfully applied to image, speech, and text.











                                                                            -

                                                                            Dual learning

                                                                            +

                                                                            Dual learning

                                                                            Dual learning is a new learning paradigm, the basic idea of which is @@ -1637,7 +1637,7 @@ image-to-text, and text-to-image.











                                                                            -

                                                                            Distributed machine learning

                                                                            +

                                                                            Distributed machine learning

                                                                            Distributed computation will speed up machine learning algorithms, @@ -1648,7 +1648,7 @@ implementing the machine learning algorithms in parallel is required.











                                                                            -

                                                                            Meta learning

                                                                            +

                                                                            Meta learning

                                                                            Meta learning is an emerging research direction in machine @@ -1662,7 +1662,7 @@ tasks.











                                                                            -

                                                                            The Challenges Facing Machine Learning

                                                                            +

                                                                            The Challenges Facing Machine Learning

                                                                            While there has been much progress in machine learning, there are also challenges. @@ -1690,7 +1690,7 @@ See the article on Explainable machine learning +

                                                                            Explainable machine learning

                                                                            Machine learning, especially deep learning, evolves rapidly. The @@ -1718,7 +1718,7 @@ problems by logical reasoning.











                                                                            -

                                                                            Quantum machine learning

                                                                            +

                                                                            Quantum machine learning

                                                                            Quantum machine learning is an emerging interdisciplinary research @@ -1749,7 +1749,7 @@ computing systems.











                                                                            -

                                                                            Quantum machine learning algorithms based on linear algebra

                                                                            +

                                                                            Quantum machine learning algorithms based on linear algebra

                                                                            Many quantum machine learning algorithms are based on variants of @@ -1768,7 +1768,7 @@ input data into a quantum system is as yet unknown for most cases.











                                                                            -

                                                                            Quantum reinforcement learning

                                                                            +

                                                                            Quantum reinforcement learning

                                                                            In quantum reinforcement learning, a quantum agent interacts with the @@ -1782,7 +1782,7 @@ superconducting circuits and systems of trapped ions.











                                                                            -

                                                                            Quantum deep learning

                                                                            +

                                                                            Quantum deep learning

                                                                            Dedicated quantum information processors, such as quantum annealers @@ -1801,7 +1801,7 @@ result.











                                                                            -

                                                                            Social machine learning

                                                                            +

                                                                            Social machine learning

                                                                            Machine learning aims to imitate how humans @@ -1819,7 +1819,7 @@ And much more.











                                                                            -

                                                                            The last words?

                                                                            +

                                                                            The last words?

                                                                            Early computer scientist Alan Kay said, The best way to predict the @@ -1831,7 +1831,7 @@ topics. Together, we will not just predict the future, but create it.











                                                                            -

                                                                            Best wishes to you all and thanks so much for your heroic efforts this semester

                                                                            +

                                                                            Best wishes to you all and thanks so much for your heroic efforts this semester





                                                                            diff --git a/doc/pub/week48/ipynb/ipynb-week48-src.tar.gz b/doc/pub/week48/ipynb/ipynb-week48-src.tar.gz index 2d95c81adea8cb54ffc52e547525b831a4b0a439..5df6a25bd2d57d57545acb8fd4c683c5fc7f3d65 100644 GIT binary patch delta 53 zcmaDg%jnfCBR2VN4u;BKjcl!KjIC@;t!&J#Y%Hy8tgUQ75%yL#j#f6#RyM9yHtwx# HJSn{ZvPupT delta 53 zcmaDg%jnfCBR2VN4hF?Zjcl!KjIC@;t!&J#Y%Hy8tgUQ75%yL#j#f6#RyM9yHtwx# HJSn{ZlQIob diff --git a/doc/pub/week48/ipynb/week48.ipynb b/doc/pub/week48/ipynb/week48.ipynb index cc4da5784..e0294ea2a 100644 --- a/doc/pub/week48/ipynb/week48.ipynb +++ b/doc/pub/week48/ipynb/week48.ipynb @@ -66,19 +66,19 @@ "wavelets, splines etc.\n", "\n", "If our feature space is not easy to separate, as shown in the figure\n", - "<<<<<<< HEAD\n", - "here, we can achieve a better separation by introducing more complex\n", - "basis functions. The ideal would be, as shown in the next figure, to,\n", + "generated by the code below, we can achieve a better separation by introducing a more complex\n", + "basis functions. The ideal would be, as shown by the code example below, to,\n", "via a specific transformation to obtain a separation between the\n", - "classes which is almost linear.\n", - "=======\n", - "here generated by the code below (see also Figures 12.2 and 12.3 of [Hastie et al.](https://www.springer.com/gp/book/9780387848570)), we can achieve a better separation by introducing more complex\n", + "classes that is almost linear. See also Figures 12.2 and 12.3 of [Hastie et al.](https://www.springer.com/gp/book/9780387848570).\n", + "\n", + "We can achieve a better separation by introducing more complex\n", "basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to \n", "obtain a separation between the classes which is almost linear. \n", - ">>>>>>> origin/master\n", "\n", - "The change of basis, from $x\\rightarrow z=\\phi(x)$ leads to the same type of equations to be solved, except that\n", - "we need to introduce, for example, a polynomial transformation to a two-dimensional training set." + "The change of basis, from $x\\rightarrow z=\\phi(x)$ leads to the same\n", + "type of equations to be solved, except that we need to introduce, for\n", + "example, a polynomial transformation to a two-dimensional training\n", + "set." ] }, { @@ -831,7 +831,7 @@ "\n", "## Setting up $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq \\boldsymbol{h}$\n", "\n", - "We have two constraints, $0\\le \\lambda_i$ and $\\lambda_i \\le C$. To do this we multiply the ones with the contraint\n", + "We have two constraints, $0\\le \\lambda_i$ and $\\lambda_i \\le C$. To do this we multiply the ones with the constraint\n", "$\\ge$ with $-1$ in order to get $\\le$. It means that the problem $\\boldsymbol{G}\\boldsymbol{\\lambda} \\preceq \\boldsymbol{h}$\n", "can be written as" ] @@ -857,7 +857,7 @@ "\\lambda_3 \\\\\n", "\\dots \\\\\n", "\\lambda_n \\\\\n", - "\\end{bmatrix}\\wedge \n", + "\\end{bmatrix} \\preceq \n", "\\begin{bmatrix} 0 \\\\\n", "0 \\\\\n", "0 \\\\\n", diff --git a/doc/src/week48/week48.do.txt b/doc/src/week48/week48.do.txt index 2cbfb57a5..b56cdec44 100644 --- a/doc/src/week48/week48.do.txt +++ b/doc/src/week48/week48.do.txt @@ -57,19 +57,19 @@ space using other basis expansions such as higher-order polynomials, wavelets, splines etc. If our feature space is not easy to separate, as shown in the figure -<<<<<<< HEAD -here, we can achieve a better separation by introducing more complex -basis functions. The ideal would be, as shown in the next figure, to, +generated by the code below, we can achieve a better separation by introducing a more complex +basis functions. The ideal would be, as shown by the code example below, to, via a specific transformation to obtain a separation between the -classes which is almost linear. -======= -here generated by the code below (see also Figures 12.2 and 12.3 of "Hastie et al.":"https://www.springer.com/gp/book/9780387848570"), we can achieve a better separation by introducing more complex +classes that is almost linear. See also Figures 12.2 and 12.3 of "Hastie et al.":"https://www.springer.com/gp/book/9780387848570". + +We can achieve a better separation by introducing more complex basis functions. The ideal would be (see Figures 12.2 and 12.3) to, via a specific transformation to obtain a separation between the classes which is almost linear. ->>>>>>> origin/master -The change of basis, from $x\rightarrow z=\phi(x)$ leads to the same type of equations to be solved, except that -we need to introduce, for example, a polynomial transformation to a two-dimensional training set. +The change of basis, from $x\rightarrow z=\phi(x)$ leads to the same +type of equations to be solved, except that we need to introduce, for +example, a polynomial transformation to a two-dimensional training +set. !bc pycod import numpy as np @@ -589,7 +589,7 @@ o To set up the matrix $\bm{G}$ we note that the inequalities $0\leq \lambda_i \ !split ===== Setting up $\bm{G}\bm{\lambda} \preceq \bm{h}$ ===== -We have two constraints, $0\le \lambda_i$ and $\lambda_i \le C$. To do this we multiply the ones with the contraint +We have two constraints, $0\le \lambda_i$ and $\lambda_i \le C$. To do this we multiply the ones with the constraint $\ge$ with $-1$ in order to get $\le$. It means that the problem $\bm{G}\bm{\lambda} \preceq \bm{h}$ can be written as !bt @@ -610,7 +610,7 @@ can be written as \lambda_3 \\ \dots \\ \lambda_n \\ -\end{bmatrix}\wedge +\end{bmatrix} \preceq \begin{bmatrix} 0 \\ 0 \\ 0 \\