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({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({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({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({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({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({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({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({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({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({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({-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({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({
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@@ -185,71 +184,70 @@ MathJax.Hub.Config({
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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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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({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({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({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({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({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({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({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({
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({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({
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({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({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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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({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({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({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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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({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? 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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({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? 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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({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({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? 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@@ -185,71 +184,70 @@ MathJax.Hub.Config({
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
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- ('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({
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
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()
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.
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 \
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.
There are several popular kernels being used. These are
@@ -446,7 +447,7 @@ in practice.
@@ -643,7 +644,7 @@ plt.show()
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
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.
We remind ourselves about the general problem we want to solve
@@ -777,7 +778,7 @@ sol['primal objective']
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
We have the general problem
@@ -824,10 +825,10 @@ $$
-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
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
+
The course has two central parts
@@ -901,7 +902,7 @@ The course has two central parts
The following topics have been discussed:
@@ -919,7 +920,7 @@ The following topics have been discussed:
The following topics will be covered
@@ -961,7 +962,7 @@ The following topics will be covered
The course introduces a variety of central algorithms and methods
@@ -988,7 +989,7 @@ ethical conduct is emphasized throughout the course.
Where to find recent results:
@@ -1023,7 +1024,7 @@ Where to find recent results:
When do we resample?
@@ -1143,7 +1144,7 @@ When do we resample?
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
+
Based on multi-layer nonlinear neural networks, deep learning can
@@ -1196,7 +1197,7 @@ networks have been proposed, such as
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
+
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
The network layers:
@@ -1360,7 +1361,7 @@ Here we take away all lateral connections between nodes in the visible layer as
The goal of the hidden layer is to increase the model's expressive
@@ -1382,7 +1383,7 @@ over (integrated out).
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.
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_
@@ -1477,7 +1478,7 @@ $$
Autoencoders are artificial neural networks capable of learning
@@ -1538,7 +1539,7 @@ See also A. Geron's textbook, chapter 15.
This is an important topic if we aim at extracting a probability
@@ -1560,7 +1561,7 @@ See also the 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.
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.
The conventional deep generative model has a potential problem: the
@@ -1633,7 +1634,7 @@ successfully applied to image, speech, and text.
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 computation will speed up machine learning algorithms,
@@ -1660,7 +1661,7 @@ implementing the machine learning algorithms in parallel is required.
Meta learning is an emerging research direction in machine
@@ -1674,7 +1675,7 @@ tasks.
While there has been much progress in machine learning, there are also challenges.
@@ -1702,7 +1703,7 @@ See the article on Explainable machine learning
+
Machine learning, especially deep learning, evolves rapidly. The
@@ -1730,7 +1731,7 @@ problems by logical reasoning.
Quantum machine learning is an emerging interdisciplinary research
@@ -1761,7 +1762,7 @@ computing systems.
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.
In quantum reinforcement learning, a quantum agent interacts with the
@@ -1794,7 +1795,7 @@ superconducting circuits and systems of trapped ions.
Dedicated quantum information processors, such as quantum annealers
@@ -1813,7 +1814,7 @@ result.
Machine learning aims to imitate how humans
@@ -1831,7 +1832,7 @@ And much more.
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.
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.
-====
-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()
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.
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 \
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.
There are several popular kernels being used. These are
@@ -478,7 +478,7 @@ in practice.
@@ -674,7 +674,7 @@ plt.show()
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
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.
We remind ourselves about the general problem we want to solve
@@ -793,7 +793,7 @@ sol['primal objective']
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
We have the general problem
@@ -835,10 +835,10 @@ $$
-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.
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
+
The course has two central parts
@@ -909,7 +909,7 @@ The course has two central parts
The following topics have been discussed:
@@ -926,7 +926,7 @@ The following topics have been discussed:
The following topics will be covered
@@ -970,7 +970,7 @@ The following topics will be covered
The course introduces a variety of central algorithms and methods
@@ -996,7 +996,7 @@ ethical conduct is emphasized throughout the course.
Where to find recent results:
@@ -1029,7 +1029,7 @@ Where to find recent results:
When do we resample?
@@ -1139,7 +1139,7 @@ When do we resample?
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
+
Based on multi-layer nonlinear neural networks, deep learning can
@@ -1189,7 +1189,7 @@ networks have been proposed, such as
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
+
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
The network layers:
@@ -1351,7 +1351,7 @@ Here we take away all lateral connections between nodes in the visible layer as
The goal of the hidden layer is to increase the model's expressive
@@ -1372,7 +1372,7 @@ over (integrated out).
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.
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_
@@ -1462,7 +1462,7 @@ $$
Autoencoders are artificial neural networks capable of learning
@@ -1521,7 +1521,7 @@ See also A. Geron's textbook, chapter 15.
This is an important topic if we aim at extracting a probability
@@ -1543,7 +1543,7 @@ See also the 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.
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.
The conventional deep generative model has a potential problem: the
@@ -1616,7 +1616,7 @@ successfully applied to image, speech, and text.
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 computation will speed up machine learning algorithms,
@@ -1643,7 +1643,7 @@ implementing the machine learning algorithms in parallel is required.
Meta learning is an emerging research direction in machine
@@ -1657,7 +1657,7 @@ tasks.
While there has been much progress in machine learning, there are also challenges.
@@ -1685,7 +1685,7 @@ See the article on Explainable machine learning
+
Machine learning, especially deep learning, evolves rapidly. The
@@ -1713,7 +1713,7 @@ problems by logical reasoning.
Quantum machine learning is an emerging interdisciplinary research
@@ -1744,7 +1744,7 @@ computing systems.
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.
In quantum reinforcement learning, a quantum agent interacts with the
@@ -1777,7 +1777,7 @@ superconducting circuits and systems of trapped ions.
Dedicated quantum information processors, such as quantum annealers
@@ -1796,7 +1796,7 @@ result.
Machine learning aims to imitate how humans
@@ -1814,7 +1814,7 @@ And much more.
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.
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.
-====
-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()
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.
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 \
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.
There are several popular kernels being used. These are
@@ -483,7 +483,7 @@ in practice.
@@ -679,7 +679,7 @@ plt.show()
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
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.
We remind ourselves about the general problem we want to solve
@@ -798,7 +798,7 @@ sol['primal objective']
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
We have the general problem
@@ -840,10 +840,10 @@ $$
-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.
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
+
The course has two central parts
@@ -914,7 +914,7 @@ The course has two central parts
The following topics have been discussed:
@@ -931,7 +931,7 @@ The following topics have been discussed:
The following topics will be covered
@@ -975,7 +975,7 @@ The following topics will be covered
The course introduces a variety of central algorithms and methods
@@ -1001,7 +1001,7 @@ ethical conduct is emphasized throughout the course.
Where to find recent results:
@@ -1034,7 +1034,7 @@ Where to find recent results:
When do we resample?
@@ -1144,7 +1144,7 @@ When do we resample?
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
+
Based on multi-layer nonlinear neural networks, deep learning can
@@ -1194,7 +1194,7 @@ networks have been proposed, such as
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
+
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
The network layers:
@@ -1356,7 +1356,7 @@ Here we take away all lateral connections between nodes in the visible layer as
The goal of the hidden layer is to increase the model's expressive
@@ -1377,7 +1377,7 @@ over (integrated out).
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.
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_
@@ -1467,7 +1467,7 @@ $$
Autoencoders are artificial neural networks capable of learning
@@ -1526,7 +1526,7 @@ See also A. Geron's textbook, chapter 15.
The equations
+The equations
The problem to solve
+The problem to solve
Tailoring the equations to the usage of CVXOPT
+Tailoring the equations to the usage of CVXOPT
Different kernels and Mercer's theorem
+Different kernels and Mercer's theorem
The moons example (Adapted from Geron, chapter 5)
+The moons example (Adapted from Geron, chapter 5)
Mathematical optimization of convex functions
+Mathematical optimization of convex functions
How do we solve these problems?
+How do we solve these problems?
A simple example
+A simple example
Back to the more realistic cases
+Back to the more realistic cases
Setting up the matrices and the problem
+Setting up the matrices and the problem
Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
+Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
@@ -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
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
Statistical analysis and optimization of data
+Statistical analysis and optimization of data
Machine learning
+Machine learning
Learning outcomes and overarching aims of this course
+Learning outcomes and overarching aims of this course
Perspective on Machine Learning
+Perspective on Machine Learning
Machine Learning Research
+Machine Learning Research
Starting your Machine Learning Project
+Starting your Machine Learning Project
Choose a Model and Algorithm
+Choose a Model and Algorithm
Preparing Your Data
+Preparing Your Data
Which Activation and Weights to Choose in Neural Networks
+Which Activation and Weights to Choose in Neural Networks
Optimization Methods and Hyperparameters
+Optimization Methods and Hyperparameters
Resampling
+Resampling
Other courses on Data science and Machine Learning at UiO
+Other courses on Data science and Machine Learning at UiO
Additional courses of interest
What's the future like?
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?
Boltzmann Machines
Some similarities and differences from DNNs
+Some similarities and differences from DNNs
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
Goals
+Goals
Joint distribution
+Joint distribution
Network Elements, the energy function
+Network Elements, the energy function
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 \( E(\mathbf{x},\mathbf{h}) \).
More about RBMs
+More about RBMs
Autoencoders: Overarching view
Bayesian Machine Learning
+Bayesian Machine Learning
Reinforcement Learning
Transfer learning
+Transfer learning
Adversarial learning
+Adversarial learning
Dual learning
+Dual learning
Distributed machine learning
+Distributed machine learning
Meta learning
+Meta learning
The Challenges Facing Machine Learning
+The Challenges Facing Machine Learning
Explainable machine learning
Quantum machine learning
+Quantum machine learning
Quantum machine learning algorithms based on linear algebra
+Quantum machine learning algorithms based on linear algebra
Quantum reinforcement learning
+Quantum reinforcement learning
Quantum deep learning
+Quantum deep learning
Social machine learning
+Social machine learning
The last words?
+The last words?
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.
-The equations
+The equations
-The problem to solve
+The problem to solve
-Tailoring the equations to the usage of CVXOPT
+Tailoring the equations to the usage of CVXOPT
-Different kernels and Mercer's theorem
+Different kernels and Mercer's theorem
-The moons example (Adapted from Geron, chapter 5)
+The moons example (Adapted from Geron, chapter 5)
-Mathematical optimization of convex functions
+Mathematical optimization of convex functions
-How do we solve these problems?
+How do we solve these problems?
-A simple example
+A simple example
-Back to the more realistic cases
+Back to the more realistic cases
-Setting up the matrices and the problem
+Setting up the matrices and the problem
-Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
+Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
-SVMs and Regression and multiclass classification
+SVMs and Regression and multiclass classification
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
-Statistical analysis and optimization of data
+Statistical analysis and optimization of data
-Machine learning
+Machine learning
-Learning outcomes and overarching aims of this course
+Learning outcomes and overarching aims of this course
-Perspective on Machine Learning
+Perspective on Machine Learning
-Machine Learning Research
+Machine Learning Research
-Starting your Machine Learning Project
+Starting your Machine Learning Project
-Choose a Model and Algorithm
+Choose a Model and Algorithm
-Preparing Your Data
+Preparing Your Data
-Which Activation and Weights to Choose in Neural Networks
+Which Activation and Weights to Choose in Neural Networks
-Optimization Methods and Hyperparameters
+Optimization Methods and Hyperparameters
-Resampling
+Resampling
-Other courses on Data science and Machine Learning at UiO
+Other courses on Data science and Machine Learning at UiO
Additional courses of interest
What's the future like?
-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?
Boltzmann Machines
-Some similarities and differences from DNNs
+Some similarities and differences from DNNs
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
-Goals
+Goals
-Joint distribution
+Joint distribution
-Network Elements, the energy function
+Network Elements, the energy function
-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 \( E(\mathbf{x},\mathbf{h}) \).
-More about RBMs
+More about RBMs
Autoencoders: Overarching view
-Bayesian Machine Learning
+Bayesian Machine Learning
Reinforcement Learning
-Transfer learning
+Transfer learning
-Adversarial learning
+Adversarial learning
-Dual learning
+Dual learning
-Distributed machine learning
+Distributed machine learning
-Meta learning
+Meta learning
-The Challenges Facing Machine Learning
+The Challenges Facing Machine Learning
Explainable machine learning
-Quantum machine learning
+Quantum machine learning
-Quantum machine learning algorithms based on linear algebra
+Quantum machine learning algorithms based on linear algebra
-Quantum reinforcement learning
+Quantum reinforcement learning
-Quantum deep learning
+Quantum deep learning
-Social machine learning
+Social machine learning
-The last words?
+The last words?
-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.
-The equations
+The equations
-The problem to solve
+The problem to solve
-Tailoring the equations to the usage of CVXOPT
+Tailoring the equations to the usage of CVXOPT
-Different kernels and Mercer's theorem
+Different kernels and Mercer's theorem
-The moons example (Adapted from Geron, chapter 5)
+The moons example (Adapted from Geron, chapter 5)
-Mathematical optimization of convex functions
+Mathematical optimization of convex functions
-How do we solve these problems?
+How do we solve these problems?
-A simple example
+A simple example
-Back to the more realistic cases
+Back to the more realistic cases
-Setting up the matrices and the problem
+Setting up the matrices and the problem
-Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
+Setting up \( \boldsymbol{G}\boldsymbol{\lambda} \preceq \boldsymbol{h} \)
-SVMs and Regression and multiclass classification
+SVMs and Regression and multiclass classification
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
-Statistical analysis and optimization of data
+Statistical analysis and optimization of data
-Machine learning
+Machine learning
-Learning outcomes and overarching aims of this course
+Learning outcomes and overarching aims of this course
-Perspective on Machine Learning
+Perspective on Machine Learning
-Machine Learning Research
+Machine Learning Research
-Starting your Machine Learning Project
+Starting your Machine Learning Project
-Choose a Model and Algorithm
+Choose a Model and Algorithm
-Preparing Your Data
+Preparing Your Data
-Which Activation and Weights to Choose in Neural Networks
+Which Activation and Weights to Choose in Neural Networks
-Optimization Methods and Hyperparameters
+Optimization Methods and Hyperparameters
-Resampling
+Resampling
-Other courses on Data science and Machine Learning at UiO
+Other courses on Data science and Machine Learning at UiO
Additional courses of interest
What's the future like?
-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?
Boltzmann Machines
-Some similarities and differences from DNNs
+Some similarities and differences from DNNs
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
-Goals
+Goals
-Joint distribution
+Joint distribution
-Network Elements, the energy function
+Network Elements, the energy function
-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 \( E(\mathbf{x},\mathbf{h}) \).
-More about RBMs
+More about RBMs
Autoencoders: Overarching view
-Bayesian Machine Learning
+Bayesian Machine Learning