added material here
This commit is contained in:
mhjensen
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
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2,
|
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None,
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||||
'___sec17')]}
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'___sec31')]}
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end of tocinfo -->
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||||
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||||
<body>
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||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
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||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
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||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
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||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
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<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
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||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
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</ul>
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</li>
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@@ -191,7 +222,7 @@ MathJax.Hub.Config({
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<li><a href="._summary-bs008.html">9</a></li>
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<li><a href="._summary-bs009.html">10</a></li>
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<li><a href="">...</a></li>
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<li><a href="._summary-bs018.html">19</a></li>
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<li><a href="._summary-bs032.html">33</a></li>
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<li><a href="._summary-bs001.html">»</a></li>
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</ul>
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<!-- ------------------- end of main content --------------- -->
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@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
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2,
|
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None,
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'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
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'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
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||||
'___sec31')]}
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||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
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||||
@@ -169,7 +200,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._summary-bs009.html">10</a></li>
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||||
<li><a href="._summary-bs010.html">11</a></li>
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||||
<li><a href="">...</a></li>
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||||
<li><a href="._summary-bs018.html">19</a></li>
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||||
<li><a href="._summary-bs032.html">33</a></li>
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||||
<li><a href="._summary-bs002.html">»</a></li>
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||||
</ul>
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||||
<!-- ------------------- end of main content --------------- -->
|
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@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
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'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -175,7 +206,7 @@ Does that match the experiences you have made this semester?
|
||||
<li><a href="._summary-bs010.html">11</a></li>
|
||||
<li><a href="._summary-bs011.html">12</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs003.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
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|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -176,7 +207,7 @@ The course has two central parts
|
||||
<li><a href="._summary-bs011.html">12</a></li>
|
||||
<li><a href="._summary-bs012.html">13</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs004.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -183,7 +214,7 @@ The following topics will be covered
|
||||
<li><a href="._summary-bs012.html">13</a></li>
|
||||
<li><a href="._summary-bs013.html">14</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs005.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
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|
||||
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|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -180,7 +211,7 @@ The following topics will be covered
|
||||
<li><a href="._summary-bs013.html">14</a></li>
|
||||
<li><a href="._summary-bs014.html">15</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs006.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -193,7 +224,7 @@ ethical conduct is emphasized throughout the course.
|
||||
<li><a href="._summary-bs014.html">15</a></li>
|
||||
<li><a href="._summary-bs015.html">16</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs007.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -185,7 +216,7 @@ Neural Networks, etc.</a>
|
||||
<li><a href="._summary-bs015.html">16</a></li>
|
||||
<li><a href="._summary-bs016.html">17</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs008.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
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|
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||||
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||||
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||||
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||||
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||||
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|
||||
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||||
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||||
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||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -184,7 +215,7 @@ Where to find recent results:
|
||||
<li><a href="._summary-bs016.html">17</a></li>
|
||||
<li><a href="._summary-bs017.html">18</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs009.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
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|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Resampling', 2, None, '___sec13'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
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|
||||
'___sec14'),
|
||||
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|
||||
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|
||||
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|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
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|
||||
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|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
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|
||||
None,
|
||||
'___sec26'),
|
||||
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|
||||
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|
||||
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|
||||
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|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
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|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -148,14 +179,14 @@ MathJax.Hub.Config({
|
||||
<a name="part0009"></a>
|
||||
<!-- !split -->
|
||||
|
||||
<h2 id="___sec8" class="anchor">Hot Topics Now </h2>
|
||||
<h2 id="___sec8" class="anchor">Starting your Machine Learning Project </h2>
|
||||
|
||||
<ol>
|
||||
<li> Boosting techniques and complex neural networks</li>
|
||||
<li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_self">Adversarial examples</a></li>
|
||||
<li> <a href="https://arxiv.org/pdf/1707.00600" target="_self">Zero shot learning</a></li>
|
||||
<li> Transfer learning</li>
|
||||
<li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_self">Model interpretability</a></li>
|
||||
<li> Identify problem type: classification, generation, regression</li>
|
||||
<li> Consider your data carefully</li>
|
||||
<li> Choose a simple model that fits 1. and 2.</li>
|
||||
<li> Consider your data carefully again… data representation</li>
|
||||
<li> Based on results, feedback loop to earliest possible point</li>
|
||||
</ol>
|
||||
|
||||
<p>
|
||||
@@ -181,6 +212,8 @@ MathJax.Hub.Config({
|
||||
<li><a href="._summary-bs016.html">17</a></li>
|
||||
<li><a href="._summary-bs017.html">18</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs010.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -58,26 +58,43 @@ Automatically generated HTML file from DocOnce source
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -123,16 +140,30 @@ MathJax.Hub.Config({
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs006.html#___sec5" style="font-size: 80%;">Learning outcomes and overarching aims of this course</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs007.html#___sec6" style="font-size: 80%;">Perspective on Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs008.html#___sec7" style="font-size: 80%;">Machine Learning Research</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs009.html#___sec8" style="font-size: 80%;">Starting your Machine Learning Project</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs010.html#___sec9" style="font-size: 80%;">Choose a Model and Algorithm</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs011.html#___sec10" style="font-size: 80%;">Preparing Your Data</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs012.html#___sec11" style="font-size: 80%;">Which Activation and Weights to Choose in Neural Networks</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs013.html#___sec12" style="font-size: 80%;">Optimization Methods and Hyperparameters</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs014.html#___sec13" style="font-size: 80%;">Resampling</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs015.html#___sec14" style="font-size: 80%;">Other courses on Data science and Machine Learning at UiO</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs016.html#___sec15" style="font-size: 80%;">Additional courses of interest</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs017.html#___sec16" style="font-size: 80%;">Hot Topics Now</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs018.html#___sec17" style="font-size: 80%;">Reinforcement Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs019.html#___sec18" style="font-size: 80%;">Transfer learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs020.html#___sec19" style="font-size: 80%;">Adversarial learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs021.html#___sec20" style="font-size: 80%;">Dual learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs022.html#___sec21" style="font-size: 80%;">Distributed machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs023.html#___sec22" style="font-size: 80%;">Meta learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs024.html#___sec23" style="font-size: 80%;">The Challenges Facing Machine Learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs025.html#___sec24" style="font-size: 80%;">Explainable machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs026.html#___sec25" style="font-size: 80%;">Quantum machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs027.html#___sec26" style="font-size: 80%;">Quantum machine learning algorithms based on linear algebra</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs028.html#___sec27" style="font-size: 80%;">Quantum reinforcement learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs029.html#___sec28" style="font-size: 80%;">Quantum deep learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs030.html#___sec29" style="font-size: 80%;">Social machine learning</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs031.html#___sec30" style="font-size: 80%;">The last words?</a></li>
|
||||
<!-- navigation toc: --> <li><a href="._summary-bs032.html#___sec31" style="font-size: 80%;">Best wishes to you all and thanks so much for your heroic efforts this semester</a></li>
|
||||
|
||||
</ul>
|
||||
</li>
|
||||
@@ -191,7 +222,7 @@ MathJax.Hub.Config({
|
||||
<li><a href="._summary-bs008.html">9</a></li>
|
||||
<li><a href="._summary-bs009.html">10</a></li>
|
||||
<li><a href="">...</a></li>
|
||||
<li><a href="._summary-bs018.html">19</a></li>
|
||||
<li><a href="._summary-bs032.html">33</a></li>
|
||||
<li><a href="._summary-bs001.html">»</a></li>
|
||||
</ul>
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
@@ -286,20 +286,7 @@ Where to find recent results:
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec8">Hot Topics Now </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> Boosting techniques and complex neural networks</li>
|
||||
<p><li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_blank">Adversarial examples</a></li>
|
||||
<p><li> <a href="https://arxiv.org/pdf/1707.00600" target="_blank">Zero shot learning</a></li>
|
||||
<p><li> Transfer learning</li>
|
||||
<p><li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_blank">Model interpretability</a></li>
|
||||
</ol>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec9">Starting your Machine Learning Project </h2>
|
||||
<h2 id="___sec8">Starting your Machine Learning Project </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> Identify problem type: classification, generation, regression</li>
|
||||
@@ -312,7 +299,7 @@ Where to find recent results:
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec10">Choose a Model and Algorithm </h2>
|
||||
<h2 id="___sec9">Choose a Model and Algorithm </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> Supervised?</li>
|
||||
@@ -323,7 +310,7 @@ Where to find recent results:
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec11">Preparing Your Data </h2>
|
||||
<h2 id="___sec10">Preparing Your Data </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> Shuffle your data</li>
|
||||
@@ -353,7 +340,7 @@ Where to find recent results:
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec12">Which Activation and Weights to Choose in Neural Networks </h2>
|
||||
<h2 id="___sec11">Which Activation and Weights to Choose in Neural Networks </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> RELU? ELU?</li>
|
||||
@@ -376,7 +363,7 @@ Where to find recent results:
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec13">Optimization Methods and Hyperparameters </h2>
|
||||
<h2 id="___sec12">Optimization Methods and Hyperparameters </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> Stochastic gradient descent
|
||||
@@ -401,7 +388,7 @@ Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifie
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec14">Resampling </h2>
|
||||
<h2 id="___sec13">Resampling </h2>
|
||||
|
||||
<p>
|
||||
When do we resample?
|
||||
@@ -415,18 +402,17 @@ When do we resample?
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec15">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
<h2 id="___sec14">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
|
||||
<ol>
|
||||
<p><li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<p><li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_blank">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 – Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
|
||||
<p><li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_blank">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 – Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 – Dyp læring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
|
||||
@@ -435,7 +421,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec16">Additional courses of interest </h2>
|
||||
<h2 id="___sec15">Additional courses of interest </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_blank">STK4051 Computational Statistics</a></li>
|
||||
@@ -445,7 +431,290 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec17">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
<h2 id="___sec16">Hot Topics Now </h2>
|
||||
|
||||
<ol>
|
||||
<p><li> Boosting techniques and complex neural networks</li>
|
||||
<p><li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_blank">Adversarial examples</a></li>
|
||||
<p><li> <a href="https://arxiv.org/pdf/1707.00600" target="_blank">Zero shot learning</a></li>
|
||||
<p><li> Transfer learning</li>
|
||||
<p><li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_blank">Model interpretability</a></li>
|
||||
</ol>
|
||||
<p>
|
||||
|
||||
Based on multi-layer nonlinear neural networks, deep learning can
|
||||
learn directly from raw data, automatically extract and abstract
|
||||
features from layer to layer, and then achieve the goal of regression,
|
||||
classification, or ranking. Deep learning has made breakthroughs in
|
||||
computer vision, speech processing and natural language, and reached
|
||||
or even surpassed human level. The success of deep learning is mainly
|
||||
due to the three factors: big data, big model, and big computing.
|
||||
|
||||
<p>
|
||||
In the past few decades, many different architectures of deep neural
|
||||
networks have been proposed, such as
|
||||
|
||||
<ol>
|
||||
<p><li> Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;</li>
|
||||
<p><li> Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;</li>
|
||||
<p><li> Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.</li>
|
||||
</ol>
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec17">Reinforcement Learning </h2>
|
||||
|
||||
<p>
|
||||
Reinforcement learning is a sub-area of machine learning. It studies
|
||||
how agents take actions based on trial and error, so as to maximize
|
||||
some notion of cumulative reward in a dynamic system or
|
||||
environment. Due to its generality, the problem has also been studied
|
||||
in many other disciplines, such as game theory, control theory,
|
||||
operations research, information theory, multi-agent systems, swarm
|
||||
intelligence, statistics, and genetic algorithms.
|
||||
|
||||
<p>
|
||||
In March 2016, AlphaGo, a computer program that plays the board game
|
||||
Go, beat Lee Sedol in a five-game match. This was the first time a
|
||||
computer Go program had beaten a 9-dan (highest rank) professional
|
||||
without handicaps. AlphaGo is based on deep convolutional neural
|
||||
networks and reinforcement learning. AlphaGo’s victory was a major
|
||||
milestone in artificial intelligence and it has also made
|
||||
reinforcement learning a hot research area in the field of machine
|
||||
learning.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec18">Transfer learning </h2>
|
||||
|
||||
<p>
|
||||
The goal of transfer learning is to transfer the model or knowledge
|
||||
obtained from a source task to the target task, in order to resolve
|
||||
the issues of insufficient training data in the target task. The
|
||||
rationality of doing so lies in that usually the source and target
|
||||
tasks have inter-correlations, and therefore either the features,
|
||||
samples, or models in the source task might provide useful information
|
||||
for us to better solve the target task. Transfer learning is a hot
|
||||
research topic in recent years, with many problems still waiting to be
|
||||
solved in this space.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec19">Adversarial learning </h2>
|
||||
|
||||
<p>
|
||||
The conventional deep generative model has a potential problem: the
|
||||
model tends to generate extreme instances to maximize the
|
||||
probabilistic likelihood, which will hurt its performance. Adversarial
|
||||
learning utilizes the adversarial behaviors (e.g., generating
|
||||
adversarial instances or training an adversarial model) to enhance the
|
||||
robustness of the model and improve the quality of the generated
|
||||
data. In recent years, one of the most promising unsupervised learning
|
||||
technologies, generative adversarial networks (GAN), has already been
|
||||
successfully applied to image, speech, and text.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec20">Dual learning </h2>
|
||||
|
||||
<p>
|
||||
Dual learning is a new learning paradigm, the basic idea of which is
|
||||
to use the primal-dual structure between machine learning tasks to
|
||||
obtain effective feedback/regularization, and guide and strengthen the
|
||||
learning process, thus reducing the requirement of large-scale labeled
|
||||
data for deep learning. The idea of dual learning has been applied to
|
||||
many problems in machine learning, including machine translation,
|
||||
image style conversion, question answering and generation, image
|
||||
classification and generation, text classification and generation,
|
||||
image-to-text, and text-to-image.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec21">Distributed machine learning </h2>
|
||||
|
||||
<p>
|
||||
Distributed computation will speed up machine learning algorithms,
|
||||
significantly improve their efficiency, and thus enlarge their
|
||||
application. When distributed meets machine learning, more than just
|
||||
implementing the machine learning algorithms in parallel is required.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec22">Meta learning </h2>
|
||||
|
||||
<p>
|
||||
Meta learning is an emerging research direction in machine
|
||||
learning. Roughly speaking, meta learning concerns learning how to
|
||||
learn, and focuses on the understanding and adaptation of the learning
|
||||
itself, instead of just completing a specific learning task. That is,
|
||||
a meta learner needs to be able to evaluate its own learning methods
|
||||
and adjust its own learning methods according to specific learning
|
||||
tasks.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec23">The Challenges Facing Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
While there has been much progress in machine learning, there are also challenges.
|
||||
|
||||
<p>
|
||||
For example, the mainstream machine learning technologies are
|
||||
black-box approaches, making us concerned about their potential
|
||||
risks. To tackle this challenge, we may want to make machine learning
|
||||
more explainable and controllable. As another example, the
|
||||
computational complexity of machine learning algorithms is usually
|
||||
very high and we may want to invent lightweight algorithms or
|
||||
implementations. Furthermore, in many domains such as physics,
|
||||
chemistry, biology, and social sciences, people usually seek elegantly
|
||||
simple equations (e.g., the Schrödinger equation) to uncover the
|
||||
underlying laws behind various phenomena. In the field of machine
|
||||
learning, can we reveal simple laws instead of designing more complex
|
||||
models for data fitting? Although there are many challenges, we are
|
||||
still very optimistic about the future of machine learning. As we look
|
||||
forward to the future, here are what we think the research hotspots in
|
||||
the next ten years will be.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec24">Explainable machine learning </h2>
|
||||
|
||||
<p>
|
||||
Machine learning, especially deep learning, evolves rapidly. The
|
||||
ability gap between machine and human on many complex cognitive tasks
|
||||
becomes narrower and narrower. However, we are still in the very early
|
||||
stage in terms of explaining why those effective models work and how
|
||||
they work.
|
||||
|
||||
<p>
|
||||
What is missing: the gap between correlation and causation Most
|
||||
machine learning techniques, especially the statistical ones, depend
|
||||
highly on data correlation to make predictions and analyses. In
|
||||
contrast, rational humans tend to reply on clear and trustworthy
|
||||
causality relations obtained via logical reasoning on real and clear
|
||||
facts. It is one of the core goals of explainable machine learning to
|
||||
transition from solving problems by data correlation to solving
|
||||
problems by logical reasoning.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec25">Quantum machine learning </h2>
|
||||
|
||||
<p>
|
||||
Quantum machine learning is an emerging interdisciplinary research
|
||||
area at the intersection of quantum computing and machine learning.
|
||||
|
||||
<p>
|
||||
Quantum computers use effects such as quantum coherence and quantum
|
||||
entanglement to process information, which is fundamentally different
|
||||
from classical computers. Quantum algorithms have surpassed the best
|
||||
classical algorithms in several problems (e.g., searching for an
|
||||
unsorted database, inverting a sparse matrix), which we call quantum
|
||||
acceleration.
|
||||
|
||||
<p>
|
||||
When quantum computing meets machine learning, it can be a mutually
|
||||
beneficial and reinforcing process, as it allows us to take advantage
|
||||
of quantum computing to improve the performance of classical machine
|
||||
learning algorithms. In addition, we can also use the machine learning
|
||||
algorithms (on classic computers) to analyze and improve quantum
|
||||
computing systems.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec26">Quantum machine learning algorithms based on linear algebra </h2>
|
||||
|
||||
<p>
|
||||
Many quantum machine learning algorithms are based on variants of
|
||||
quantum algorithms for solving linear equations, which can efficiently
|
||||
solve N-variable linear equations with complexity of O(log2 N) under
|
||||
certain conditions. The quantum matrix inversion algorithm can
|
||||
accelerate many machine learning methods, such as least square linear
|
||||
regression, least square version of support vector machine, Gaussian
|
||||
process, and more. The training of these algorithms can be simplified
|
||||
to solve linear equations. The key bottleneck of this type of quantum
|
||||
machine learning algorithms is data input—that is, how to initialize
|
||||
the quantum system with the entire data set. Although efficient
|
||||
data-input algorithms exist for certain situations, how to efficiently
|
||||
input data into a quantum system is as yet unknown for most cases.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec27">Quantum reinforcement learning </h2>
|
||||
|
||||
<p>
|
||||
In quantum reinforcement learning, a quantum agent interacts with the
|
||||
classical environment to obtain rewards from the environment, so as to
|
||||
adjust and improve its behavioral strategies. In some cases, it
|
||||
achieves quantum acceleration by the quantum processing capabilities
|
||||
of the agent or the possibility of exploring the environment through
|
||||
quantum superposition. Such algorithms have been proposed in
|
||||
superconducting circuits and systems of trapped ions.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec28">Quantum deep learning </h2>
|
||||
|
||||
<p>
|
||||
Dedicated quantum information processors, such as quantum annealers
|
||||
and programmable photonic circuits, are well suited for building deep
|
||||
quantum networks. The simplest deep quantum network is the Boltzmann
|
||||
machine. The classical Boltzmann machine consists of bits with tunable
|
||||
interactions and is trained by adjusting the interaction of these bits
|
||||
so that the distribution of its expression conforms to the statistics
|
||||
of the data. To quantize the Boltzmann machine, the neural network can
|
||||
simply be represented as a set of interacting quantum spins that
|
||||
correspond to an adjustable Ising model. Then, by initializing the
|
||||
input neurons in the Boltzmann machine to a fixed state and allowing
|
||||
the system to heat up, we can read out the output qubits to get the
|
||||
result.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec29">Social machine learning </h2>
|
||||
|
||||
<p>
|
||||
Machine learning aims to imitate how humans
|
||||
learn. While we have developed successful machine learning algorithms,
|
||||
until now we have ignored one important fact: humans are social. Each
|
||||
of us is one part of the total society and it is difficult for us to
|
||||
live, learn, and improve ourselves, alone and isolated. Therefore, we
|
||||
should design machines with social properties. Can we let machines
|
||||
evolve by imitating human society so as to achieve more effective,
|
||||
intelligent, interpretable “social machine learning”?
|
||||
|
||||
<p>
|
||||
And much more.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec30">The last words? </h2>
|
||||
|
||||
<p>
|
||||
Early computer scientist Alan Kay said, <b>The best way to predict the
|
||||
future is to create it</b>. Therefore, all machine learning
|
||||
practitioners, whether scholars or engineers, professors or students,
|
||||
need to work together to advance these important research
|
||||
topics. Together, we will not just predict the future, but create it.
|
||||
</section>
|
||||
|
||||
|
||||
<section>
|
||||
<h2 id="___sec31">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
|
||||
<p>
|
||||
<br /><br /><center><p><img src="figures/Nebbdyr2.png" align="bottom" width=500></p></center><br /><br />
|
||||
|
||||
@@ -52,26 +52,43 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -239,19 +256,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec8">Hot Topics Now </h2>
|
||||
|
||||
<ol>
|
||||
<li> Boosting techniques and complex neural networks</li>
|
||||
<li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_blank">Adversarial examples</a></li>
|
||||
<li> <a href="https://arxiv.org/pdf/1707.00600" target="_blank">Zero shot learning</a></li>
|
||||
<li> Transfer learning</li>
|
||||
<li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_blank">Model interpretability</a></li>
|
||||
</ol>
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec9">Starting your Machine Learning Project </h2>
|
||||
<h2 id="___sec8">Starting your Machine Learning Project </h2>
|
||||
|
||||
<ol>
|
||||
<li> Identify problem type: classification, generation, regression</li>
|
||||
@@ -263,7 +268,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec10">Choose a Model and Algorithm </h2>
|
||||
<h2 id="___sec9">Choose a Model and Algorithm </h2>
|
||||
|
||||
<ol>
|
||||
<li> Supervised?</li>
|
||||
@@ -273,7 +278,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec11">Preparing Your Data </h2>
|
||||
<h2 id="___sec10">Preparing Your Data </h2>
|
||||
|
||||
<ol>
|
||||
<li> Shuffle your data</li>
|
||||
@@ -301,7 +306,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec12">Which Activation and Weights to Choose in Neural Networks </h2>
|
||||
<h2 id="___sec11">Which Activation and Weights to Choose in Neural Networks </h2>
|
||||
|
||||
<ol>
|
||||
<li> RELU? ELU?</li>
|
||||
@@ -322,7 +327,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec13">Optimization Methods and Hyperparameters </h2>
|
||||
<h2 id="___sec12">Optimization Methods and Hyperparameters </h2>
|
||||
|
||||
<ol>
|
||||
<li> Stochastic gradient descent
|
||||
@@ -345,7 +350,7 @@ Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifie
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec14">Resampling </h2>
|
||||
<h2 id="___sec13">Resampling </h2>
|
||||
|
||||
<p>
|
||||
When do we resample?
|
||||
@@ -358,18 +363,17 @@ When do we resample?
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec15">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
<h2 id="___sec14">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
|
||||
<ol>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_blank">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 – Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_blank">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 – Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 – Dyp læring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
|
||||
@@ -377,7 +381,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec16">Additional courses of interest </h2>
|
||||
<h2 id="___sec15">Additional courses of interest </h2>
|
||||
|
||||
<ol>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_blank">STK4051 Computational Statistics</a></li>
|
||||
@@ -386,13 +390,292 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec17">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
<h2 id="___sec16">Hot Topics Now </h2>
|
||||
|
||||
<ol>
|
||||
<li> Boosting techniques and complex neural networks</li>
|
||||
<li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_blank">Adversarial examples</a></li>
|
||||
<li> <a href="https://arxiv.org/pdf/1707.00600" target="_blank">Zero shot learning</a></li>
|
||||
<li> Transfer learning</li>
|
||||
<li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_blank">Model interpretability</a></li>
|
||||
</ol>
|
||||
|
||||
Based on multi-layer nonlinear neural networks, deep learning can
|
||||
learn directly from raw data, automatically extract and abstract
|
||||
features from layer to layer, and then achieve the goal of regression,
|
||||
classification, or ranking. Deep learning has made breakthroughs in
|
||||
computer vision, speech processing and natural language, and reached
|
||||
or even surpassed human level. The success of deep learning is mainly
|
||||
due to the three factors: big data, big model, and big computing.
|
||||
|
||||
<p>
|
||||
In the past few decades, many different architectures of deep neural
|
||||
networks have been proposed, such as
|
||||
|
||||
<ol>
|
||||
<li> Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;</li>
|
||||
<li> Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;</li>
|
||||
<li> Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.</li>
|
||||
</ol>
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec17">Reinforcement Learning </h2>
|
||||
|
||||
<p>
|
||||
Reinforcement learning is a sub-area of machine learning. It studies
|
||||
how agents take actions based on trial and error, so as to maximize
|
||||
some notion of cumulative reward in a dynamic system or
|
||||
environment. Due to its generality, the problem has also been studied
|
||||
in many other disciplines, such as game theory, control theory,
|
||||
operations research, information theory, multi-agent systems, swarm
|
||||
intelligence, statistics, and genetic algorithms.
|
||||
|
||||
<p>
|
||||
In March 2016, AlphaGo, a computer program that plays the board game
|
||||
Go, beat Lee Sedol in a five-game match. This was the first time a
|
||||
computer Go program had beaten a 9-dan (highest rank) professional
|
||||
without handicaps. AlphaGo is based on deep convolutional neural
|
||||
networks and reinforcement learning. AlphaGo’s victory was a major
|
||||
milestone in artificial intelligence and it has also made
|
||||
reinforcement learning a hot research area in the field of machine
|
||||
learning.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec18">Transfer learning </h2>
|
||||
|
||||
<p>
|
||||
The goal of transfer learning is to transfer the model or knowledge
|
||||
obtained from a source task to the target task, in order to resolve
|
||||
the issues of insufficient training data in the target task. The
|
||||
rationality of doing so lies in that usually the source and target
|
||||
tasks have inter-correlations, and therefore either the features,
|
||||
samples, or models in the source task might provide useful information
|
||||
for us to better solve the target task. Transfer learning is a hot
|
||||
research topic in recent years, with many problems still waiting to be
|
||||
solved in this space.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec19">Adversarial learning </h2>
|
||||
|
||||
<p>
|
||||
The conventional deep generative model has a potential problem: the
|
||||
model tends to generate extreme instances to maximize the
|
||||
probabilistic likelihood, which will hurt its performance. Adversarial
|
||||
learning utilizes the adversarial behaviors (e.g., generating
|
||||
adversarial instances or training an adversarial model) to enhance the
|
||||
robustness of the model and improve the quality of the generated
|
||||
data. In recent years, one of the most promising unsupervised learning
|
||||
technologies, generative adversarial networks (GAN), has already been
|
||||
successfully applied to image, speech, and text.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec20">Dual learning </h2>
|
||||
|
||||
<p>
|
||||
Dual learning is a new learning paradigm, the basic idea of which is
|
||||
to use the primal-dual structure between machine learning tasks to
|
||||
obtain effective feedback/regularization, and guide and strengthen the
|
||||
learning process, thus reducing the requirement of large-scale labeled
|
||||
data for deep learning. The idea of dual learning has been applied to
|
||||
many problems in machine learning, including machine translation,
|
||||
image style conversion, question answering and generation, image
|
||||
classification and generation, text classification and generation,
|
||||
image-to-text, and text-to-image.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec21">Distributed machine learning </h2>
|
||||
|
||||
<p>
|
||||
Distributed computation will speed up machine learning algorithms,
|
||||
significantly improve their efficiency, and thus enlarge their
|
||||
application. When distributed meets machine learning, more than just
|
||||
implementing the machine learning algorithms in parallel is required.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec22">Meta learning </h2>
|
||||
|
||||
<p>
|
||||
Meta learning is an emerging research direction in machine
|
||||
learning. Roughly speaking, meta learning concerns learning how to
|
||||
learn, and focuses on the understanding and adaptation of the learning
|
||||
itself, instead of just completing a specific learning task. That is,
|
||||
a meta learner needs to be able to evaluate its own learning methods
|
||||
and adjust its own learning methods according to specific learning
|
||||
tasks.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec23">The Challenges Facing Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
While there has been much progress in machine learning, there are also challenges.
|
||||
|
||||
<p>
|
||||
For example, the mainstream machine learning technologies are
|
||||
black-box approaches, making us concerned about their potential
|
||||
risks. To tackle this challenge, we may want to make machine learning
|
||||
more explainable and controllable. As another example, the
|
||||
computational complexity of machine learning algorithms is usually
|
||||
very high and we may want to invent lightweight algorithms or
|
||||
implementations. Furthermore, in many domains such as physics,
|
||||
chemistry, biology, and social sciences, people usually seek elegantly
|
||||
simple equations (e.g., the Schrödinger equation) to uncover the
|
||||
underlying laws behind various phenomena. In the field of machine
|
||||
learning, can we reveal simple laws instead of designing more complex
|
||||
models for data fitting? Although there are many challenges, we are
|
||||
still very optimistic about the future of machine learning. As we look
|
||||
forward to the future, here are what we think the research hotspots in
|
||||
the next ten years will be.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec24">Explainable machine learning </h2>
|
||||
|
||||
<p>
|
||||
Machine learning, especially deep learning, evolves rapidly. The
|
||||
ability gap between machine and human on many complex cognitive tasks
|
||||
becomes narrower and narrower. However, we are still in the very early
|
||||
stage in terms of explaining why those effective models work and how
|
||||
they work.
|
||||
|
||||
<p>
|
||||
What is missing: the gap between correlation and causation Most
|
||||
machine learning techniques, especially the statistical ones, depend
|
||||
highly on data correlation to make predictions and analyses. In
|
||||
contrast, rational humans tend to reply on clear and trustworthy
|
||||
causality relations obtained via logical reasoning on real and clear
|
||||
facts. It is one of the core goals of explainable machine learning to
|
||||
transition from solving problems by data correlation to solving
|
||||
problems by logical reasoning.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec25">Quantum machine learning </h2>
|
||||
|
||||
<p>
|
||||
Quantum machine learning is an emerging interdisciplinary research
|
||||
area at the intersection of quantum computing and machine learning.
|
||||
|
||||
<p>
|
||||
Quantum computers use effects such as quantum coherence and quantum
|
||||
entanglement to process information, which is fundamentally different
|
||||
from classical computers. Quantum algorithms have surpassed the best
|
||||
classical algorithms in several problems (e.g., searching for an
|
||||
unsorted database, inverting a sparse matrix), which we call quantum
|
||||
acceleration.
|
||||
|
||||
<p>
|
||||
When quantum computing meets machine learning, it can be a mutually
|
||||
beneficial and reinforcing process, as it allows us to take advantage
|
||||
of quantum computing to improve the performance of classical machine
|
||||
learning algorithms. In addition, we can also use the machine learning
|
||||
algorithms (on classic computers) to analyze and improve quantum
|
||||
computing systems.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec26">Quantum machine learning algorithms based on linear algebra </h2>
|
||||
|
||||
<p>
|
||||
Many quantum machine learning algorithms are based on variants of
|
||||
quantum algorithms for solving linear equations, which can efficiently
|
||||
solve N-variable linear equations with complexity of O(log2 N) under
|
||||
certain conditions. The quantum matrix inversion algorithm can
|
||||
accelerate many machine learning methods, such as least square linear
|
||||
regression, least square version of support vector machine, Gaussian
|
||||
process, and more. The training of these algorithms can be simplified
|
||||
to solve linear equations. The key bottleneck of this type of quantum
|
||||
machine learning algorithms is data input—that is, how to initialize
|
||||
the quantum system with the entire data set. Although efficient
|
||||
data-input algorithms exist for certain situations, how to efficiently
|
||||
input data into a quantum system is as yet unknown for most cases.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec27">Quantum reinforcement learning </h2>
|
||||
|
||||
<p>
|
||||
In quantum reinforcement learning, a quantum agent interacts with the
|
||||
classical environment to obtain rewards from the environment, so as to
|
||||
adjust and improve its behavioral strategies. In some cases, it
|
||||
achieves quantum acceleration by the quantum processing capabilities
|
||||
of the agent or the possibility of exploring the environment through
|
||||
quantum superposition. Such algorithms have been proposed in
|
||||
superconducting circuits and systems of trapped ions.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec28">Quantum deep learning </h2>
|
||||
|
||||
<p>
|
||||
Dedicated quantum information processors, such as quantum annealers
|
||||
and programmable photonic circuits, are well suited for building deep
|
||||
quantum networks. The simplest deep quantum network is the Boltzmann
|
||||
machine. The classical Boltzmann machine consists of bits with tunable
|
||||
interactions and is trained by adjusting the interaction of these bits
|
||||
so that the distribution of its expression conforms to the statistics
|
||||
of the data. To quantize the Boltzmann machine, the neural network can
|
||||
simply be represented as a set of interacting quantum spins that
|
||||
correspond to an adjustable Ising model. Then, by initializing the
|
||||
input neurons in the Boltzmann machine to a fixed state and allowing
|
||||
the system to heat up, we can read out the output qubits to get the
|
||||
result.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec29">Social machine learning </h2>
|
||||
|
||||
<p>
|
||||
Machine learning aims to imitate how humans
|
||||
learn. While we have developed successful machine learning algorithms,
|
||||
until now we have ignored one important fact: humans are social. Each
|
||||
of us is one part of the total society and it is difficult for us to
|
||||
live, learn, and improve ourselves, alone and isolated. Therefore, we
|
||||
should design machines with social properties. Can we let machines
|
||||
evolve by imitating human society so as to achieve more effective,
|
||||
intelligent, interpretable “social machine learning”?
|
||||
|
||||
<p>
|
||||
And much more.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec30">The last words? </h2>
|
||||
|
||||
<p>
|
||||
Early computer scientist Alan Kay said, <b>The best way to predict the
|
||||
future is to create it</b>. Therefore, all machine learning
|
||||
practitioners, whether scholars or engineers, professors or students,
|
||||
need to work together to advance these important research
|
||||
topics. Together, we will not just predict the future, but create it.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec31">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
|
||||
<p>
|
||||
<br /><br /><center><p><img src="figures/Nebbdyr2.png" align="bottom" width=500></p></center><br /><br />
|
||||
|
||||
<p>
|
||||
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
|
||||
|
||||
@@ -57,26 +57,43 @@ div { text-align: justify; text-justify: inter-word; }
|
||||
'___sec5'),
|
||||
('Perspective on Machine Learning', 2, None, '___sec6'),
|
||||
('Machine Learning Research', 2, None, '___sec7'),
|
||||
('Hot Topics Now', 2, None, '___sec8'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec9'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec10'),
|
||||
('Preparing Your Data', 2, None, '___sec11'),
|
||||
('Starting your Machine Learning Project', 2, None, '___sec8'),
|
||||
('Choose a Model and Algorithm', 2, None, '___sec9'),
|
||||
('Preparing Your Data', 2, None, '___sec10'),
|
||||
('Which Activation and Weights to Choose in Neural Networks',
|
||||
2,
|
||||
None,
|
||||
'___sec12'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec13'),
|
||||
('Resampling', 2, None, '___sec14'),
|
||||
'___sec11'),
|
||||
('Optimization Methods and Hyperparameters', 2, None, '___sec12'),
|
||||
('Resampling', 2, None, '___sec13'),
|
||||
('Other courses on Data science and Machine Learning at UiO',
|
||||
2,
|
||||
None,
|
||||
'___sec15'),
|
||||
('Additional courses of interest', 2, None, '___sec16'),
|
||||
'___sec14'),
|
||||
('Additional courses of interest', 2, None, '___sec15'),
|
||||
('Hot Topics Now', 2, None, '___sec16'),
|
||||
('Reinforcement Learning', 2, None, '___sec17'),
|
||||
('Transfer learning', 2, None, '___sec18'),
|
||||
('Adversarial learning', 2, None, '___sec19'),
|
||||
('Dual learning', 2, None, '___sec20'),
|
||||
('Distributed machine learning', 2, None, '___sec21'),
|
||||
('Meta learning', 2, None, '___sec22'),
|
||||
('The Challenges Facing Machine Learning', 2, None, '___sec23'),
|
||||
('Explainable machine learning', 2, None, '___sec24'),
|
||||
('Quantum machine learning', 2, None, '___sec25'),
|
||||
('Quantum machine learning algorithms based on linear algebra',
|
||||
2,
|
||||
None,
|
||||
'___sec26'),
|
||||
('Quantum reinforcement learning', 2, None, '___sec27'),
|
||||
('Quantum deep learning', 2, None, '___sec28'),
|
||||
('Social machine learning', 2, None, '___sec29'),
|
||||
('The last words?', 2, None, '___sec30'),
|
||||
('Best wishes to you all and thanks so much for your heroic '
|
||||
'efforts this semester',
|
||||
2,
|
||||
None,
|
||||
'___sec17')]}
|
||||
'___sec31')]}
|
||||
end of tocinfo -->
|
||||
|
||||
<body>
|
||||
@@ -244,19 +261,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec8">Hot Topics Now </h2>
|
||||
|
||||
<ol>
|
||||
<li> Boosting techniques and complex neural networks</li>
|
||||
<li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_blank">Adversarial examples</a></li>
|
||||
<li> <a href="https://arxiv.org/pdf/1707.00600" target="_blank">Zero shot learning</a></li>
|
||||
<li> Transfer learning</li>
|
||||
<li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_blank">Model interpretability</a></li>
|
||||
</ol>
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec9">Starting your Machine Learning Project </h2>
|
||||
<h2 id="___sec8">Starting your Machine Learning Project </h2>
|
||||
|
||||
<ol>
|
||||
<li> Identify problem type: classification, generation, regression</li>
|
||||
@@ -268,7 +273,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec10">Choose a Model and Algorithm </h2>
|
||||
<h2 id="___sec9">Choose a Model and Algorithm </h2>
|
||||
|
||||
<ol>
|
||||
<li> Supervised?</li>
|
||||
@@ -278,7 +283,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec11">Preparing Your Data </h2>
|
||||
<h2 id="___sec10">Preparing Your Data </h2>
|
||||
|
||||
<ol>
|
||||
<li> Shuffle your data</li>
|
||||
@@ -306,7 +311,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec12">Which Activation and Weights to Choose in Neural Networks </h2>
|
||||
<h2 id="___sec11">Which Activation and Weights to Choose in Neural Networks </h2>
|
||||
|
||||
<ol>
|
||||
<li> RELU? ELU?</li>
|
||||
@@ -327,7 +332,7 @@ Where to find recent results:
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec13">Optimization Methods and Hyperparameters </h2>
|
||||
<h2 id="___sec12">Optimization Methods and Hyperparameters </h2>
|
||||
|
||||
<ol>
|
||||
<li> Stochastic gradient descent
|
||||
@@ -350,7 +355,7 @@ Which regularization and hyperparameters? \( L_1 \) or \( L_2 \), soft classifie
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec14">Resampling </h2>
|
||||
<h2 id="___sec13">Resampling </h2>
|
||||
|
||||
<p>
|
||||
When do we resample?
|
||||
@@ -363,18 +368,17 @@ When do we resample?
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec15">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
<h2 id="___sec14">Other courses on Data science and Machine Learning at UiO </h2>
|
||||
|
||||
<p>
|
||||
The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" target="_blank"><tt>https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/</tt></a> gives an excellent overview of courses on Machine learning at UiO.
|
||||
|
||||
<ol>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html" target="_blank">STK2100 Machine learning and statistical methods for prediction and classification</a>.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html" target="_blank">IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning</a>. Introductory course in machine learning and AI with an algorithmic approach.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html" target="_blank">STK-INF3000/4000 Selected Topics in Data Science</a>. The course provides insight into selected contemporary relevant topics within Data Science.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html" target="_blank">IN4080 Natural Language Processing</a>. Probabilistic and machine learning techniques applied to natural language processing.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html" target="_blank">STK-IN4300 – Statistical learning methods in Data Science</a>. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.</li>
|
||||
<li> <a href="http://www.uio.no/studier/emner/matnat/ifi/INF4490/" target="_blank">INF4490 Biologically Inspired Computing</a>. An introduction to self-adapting methods also called artificial intelligence or machine learning.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html" target="_blank">IN-STK5000 Adaptive Methods for Data-Based Decision Making</a>. Methods for adaptive collection and processing of data based on machine learning techniques.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/ifi/IN5400/" target="_blank">IN5400/INF5860 – Machine Learning for Image Analysis</a>. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.</li>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/its/TEK5040/" target="_blank">TEK5040 – Dyp læring for autonome systemer</a>. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.</li>
|
||||
@@ -382,7 +386,7 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec16">Additional courses of interest </h2>
|
||||
<h2 id="___sec15">Additional courses of interest </h2>
|
||||
|
||||
<ol>
|
||||
<li> <a href="https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html" target="_blank">STK4051 Computational Statistics</a></li>
|
||||
@@ -391,13 +395,292 @@ The link here <a href="https://www.mn.uio.no/english/research/about/centre-focus
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec17">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
<h2 id="___sec16">Hot Topics Now </h2>
|
||||
|
||||
<ol>
|
||||
<li> Boosting techniques and complex neural networks</li>
|
||||
<li> <a href="https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8" target="_blank">Adversarial examples</a></li>
|
||||
<li> <a href="https://arxiv.org/pdf/1707.00600" target="_blank">Zero shot learning</a></li>
|
||||
<li> Transfer learning</li>
|
||||
<li> <a href="https://christophm.github.io/interpretable-mlbook/interpretability.html" target="_blank">Model interpretability</a></li>
|
||||
</ol>
|
||||
|
||||
Based on multi-layer nonlinear neural networks, deep learning can
|
||||
learn directly from raw data, automatically extract and abstract
|
||||
features from layer to layer, and then achieve the goal of regression,
|
||||
classification, or ranking. Deep learning has made breakthroughs in
|
||||
computer vision, speech processing and natural language, and reached
|
||||
or even surpassed human level. The success of deep learning is mainly
|
||||
due to the three factors: big data, big model, and big computing.
|
||||
|
||||
<p>
|
||||
In the past few decades, many different architectures of deep neural
|
||||
networks have been proposed, such as
|
||||
|
||||
<ol>
|
||||
<li> Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;</li>
|
||||
<li> Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;</li>
|
||||
<li> Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.</li>
|
||||
</ol>
|
||||
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec17">Reinforcement Learning </h2>
|
||||
|
||||
<p>
|
||||
Reinforcement learning is a sub-area of machine learning. It studies
|
||||
how agents take actions based on trial and error, so as to maximize
|
||||
some notion of cumulative reward in a dynamic system or
|
||||
environment. Due to its generality, the problem has also been studied
|
||||
in many other disciplines, such as game theory, control theory,
|
||||
operations research, information theory, multi-agent systems, swarm
|
||||
intelligence, statistics, and genetic algorithms.
|
||||
|
||||
<p>
|
||||
In March 2016, AlphaGo, a computer program that plays the board game
|
||||
Go, beat Lee Sedol in a five-game match. This was the first time a
|
||||
computer Go program had beaten a 9-dan (highest rank) professional
|
||||
without handicaps. AlphaGo is based on deep convolutional neural
|
||||
networks and reinforcement learning. AlphaGo’s victory was a major
|
||||
milestone in artificial intelligence and it has also made
|
||||
reinforcement learning a hot research area in the field of machine
|
||||
learning.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec18">Transfer learning </h2>
|
||||
|
||||
<p>
|
||||
The goal of transfer learning is to transfer the model or knowledge
|
||||
obtained from a source task to the target task, in order to resolve
|
||||
the issues of insufficient training data in the target task. The
|
||||
rationality of doing so lies in that usually the source and target
|
||||
tasks have inter-correlations, and therefore either the features,
|
||||
samples, or models in the source task might provide useful information
|
||||
for us to better solve the target task. Transfer learning is a hot
|
||||
research topic in recent years, with many problems still waiting to be
|
||||
solved in this space.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec19">Adversarial learning </h2>
|
||||
|
||||
<p>
|
||||
The conventional deep generative model has a potential problem: the
|
||||
model tends to generate extreme instances to maximize the
|
||||
probabilistic likelihood, which will hurt its performance. Adversarial
|
||||
learning utilizes the adversarial behaviors (e.g., generating
|
||||
adversarial instances or training an adversarial model) to enhance the
|
||||
robustness of the model and improve the quality of the generated
|
||||
data. In recent years, one of the most promising unsupervised learning
|
||||
technologies, generative adversarial networks (GAN), has already been
|
||||
successfully applied to image, speech, and text.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec20">Dual learning </h2>
|
||||
|
||||
<p>
|
||||
Dual learning is a new learning paradigm, the basic idea of which is
|
||||
to use the primal-dual structure between machine learning tasks to
|
||||
obtain effective feedback/regularization, and guide and strengthen the
|
||||
learning process, thus reducing the requirement of large-scale labeled
|
||||
data for deep learning. The idea of dual learning has been applied to
|
||||
many problems in machine learning, including machine translation,
|
||||
image style conversion, question answering and generation, image
|
||||
classification and generation, text classification and generation,
|
||||
image-to-text, and text-to-image.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec21">Distributed machine learning </h2>
|
||||
|
||||
<p>
|
||||
Distributed computation will speed up machine learning algorithms,
|
||||
significantly improve their efficiency, and thus enlarge their
|
||||
application. When distributed meets machine learning, more than just
|
||||
implementing the machine learning algorithms in parallel is required.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec22">Meta learning </h2>
|
||||
|
||||
<p>
|
||||
Meta learning is an emerging research direction in machine
|
||||
learning. Roughly speaking, meta learning concerns learning how to
|
||||
learn, and focuses on the understanding and adaptation of the learning
|
||||
itself, instead of just completing a specific learning task. That is,
|
||||
a meta learner needs to be able to evaluate its own learning methods
|
||||
and adjust its own learning methods according to specific learning
|
||||
tasks.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec23">The Challenges Facing Machine Learning </h2>
|
||||
|
||||
<p>
|
||||
While there has been much progress in machine learning, there are also challenges.
|
||||
|
||||
<p>
|
||||
For example, the mainstream machine learning technologies are
|
||||
black-box approaches, making us concerned about their potential
|
||||
risks. To tackle this challenge, we may want to make machine learning
|
||||
more explainable and controllable. As another example, the
|
||||
computational complexity of machine learning algorithms is usually
|
||||
very high and we may want to invent lightweight algorithms or
|
||||
implementations. Furthermore, in many domains such as physics,
|
||||
chemistry, biology, and social sciences, people usually seek elegantly
|
||||
simple equations (e.g., the Schrödinger equation) to uncover the
|
||||
underlying laws behind various phenomena. In the field of machine
|
||||
learning, can we reveal simple laws instead of designing more complex
|
||||
models for data fitting? Although there are many challenges, we are
|
||||
still very optimistic about the future of machine learning. As we look
|
||||
forward to the future, here are what we think the research hotspots in
|
||||
the next ten years will be.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec24">Explainable machine learning </h2>
|
||||
|
||||
<p>
|
||||
Machine learning, especially deep learning, evolves rapidly. The
|
||||
ability gap between machine and human on many complex cognitive tasks
|
||||
becomes narrower and narrower. However, we are still in the very early
|
||||
stage in terms of explaining why those effective models work and how
|
||||
they work.
|
||||
|
||||
<p>
|
||||
What is missing: the gap between correlation and causation Most
|
||||
machine learning techniques, especially the statistical ones, depend
|
||||
highly on data correlation to make predictions and analyses. In
|
||||
contrast, rational humans tend to reply on clear and trustworthy
|
||||
causality relations obtained via logical reasoning on real and clear
|
||||
facts. It is one of the core goals of explainable machine learning to
|
||||
transition from solving problems by data correlation to solving
|
||||
problems by logical reasoning.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec25">Quantum machine learning </h2>
|
||||
|
||||
<p>
|
||||
Quantum machine learning is an emerging interdisciplinary research
|
||||
area at the intersection of quantum computing and machine learning.
|
||||
|
||||
<p>
|
||||
Quantum computers use effects such as quantum coherence and quantum
|
||||
entanglement to process information, which is fundamentally different
|
||||
from classical computers. Quantum algorithms have surpassed the best
|
||||
classical algorithms in several problems (e.g., searching for an
|
||||
unsorted database, inverting a sparse matrix), which we call quantum
|
||||
acceleration.
|
||||
|
||||
<p>
|
||||
When quantum computing meets machine learning, it can be a mutually
|
||||
beneficial and reinforcing process, as it allows us to take advantage
|
||||
of quantum computing to improve the performance of classical machine
|
||||
learning algorithms. In addition, we can also use the machine learning
|
||||
algorithms (on classic computers) to analyze and improve quantum
|
||||
computing systems.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec26">Quantum machine learning algorithms based on linear algebra </h2>
|
||||
|
||||
<p>
|
||||
Many quantum machine learning algorithms are based on variants of
|
||||
quantum algorithms for solving linear equations, which can efficiently
|
||||
solve N-variable linear equations with complexity of O(log2 N) under
|
||||
certain conditions. The quantum matrix inversion algorithm can
|
||||
accelerate many machine learning methods, such as least square linear
|
||||
regression, least square version of support vector machine, Gaussian
|
||||
process, and more. The training of these algorithms can be simplified
|
||||
to solve linear equations. The key bottleneck of this type of quantum
|
||||
machine learning algorithms is data input—that is, how to initialize
|
||||
the quantum system with the entire data set. Although efficient
|
||||
data-input algorithms exist for certain situations, how to efficiently
|
||||
input data into a quantum system is as yet unknown for most cases.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec27">Quantum reinforcement learning </h2>
|
||||
|
||||
<p>
|
||||
In quantum reinforcement learning, a quantum agent interacts with the
|
||||
classical environment to obtain rewards from the environment, so as to
|
||||
adjust and improve its behavioral strategies. In some cases, it
|
||||
achieves quantum acceleration by the quantum processing capabilities
|
||||
of the agent or the possibility of exploring the environment through
|
||||
quantum superposition. Such algorithms have been proposed in
|
||||
superconducting circuits and systems of trapped ions.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec28">Quantum deep learning </h2>
|
||||
|
||||
<p>
|
||||
Dedicated quantum information processors, such as quantum annealers
|
||||
and programmable photonic circuits, are well suited for building deep
|
||||
quantum networks. The simplest deep quantum network is the Boltzmann
|
||||
machine. The classical Boltzmann machine consists of bits with tunable
|
||||
interactions and is trained by adjusting the interaction of these bits
|
||||
so that the distribution of its expression conforms to the statistics
|
||||
of the data. To quantize the Boltzmann machine, the neural network can
|
||||
simply be represented as a set of interacting quantum spins that
|
||||
correspond to an adjustable Ising model. Then, by initializing the
|
||||
input neurons in the Boltzmann machine to a fixed state and allowing
|
||||
the system to heat up, we can read out the output qubits to get the
|
||||
result.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec29">Social machine learning </h2>
|
||||
|
||||
<p>
|
||||
Machine learning aims to imitate how humans
|
||||
learn. While we have developed successful machine learning algorithms,
|
||||
until now we have ignored one important fact: humans are social. Each
|
||||
of us is one part of the total society and it is difficult for us to
|
||||
live, learn, and improve ourselves, alone and isolated. Therefore, we
|
||||
should design machines with social properties. Can we let machines
|
||||
evolve by imitating human society so as to achieve more effective,
|
||||
intelligent, interpretable “social machine learning”?
|
||||
|
||||
<p>
|
||||
And much more.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec30">The last words? </h2>
|
||||
|
||||
<p>
|
||||
Early computer scientist Alan Kay said, <b>The best way to predict the
|
||||
future is to create it</b>. Therefore, all machine learning
|
||||
practitioners, whether scholars or engineers, professors or students,
|
||||
need to work together to advance these important research
|
||||
topics. Together, we will not just predict the future, but create it.
|
||||
|
||||
<p>
|
||||
<!-- !split --><br><br><br><br><br><br><br><br><br><br>
|
||||
|
||||
<h2 id="___sec31">Best wishes to you all and thanks so much for your heroic efforts this semester </h2>
|
||||
|
||||
<p>
|
||||
<br /><br /><center><p><img src="figures/Nebbdyr2.png" align="bottom" width=500></p></center><br /><br />
|
||||
|
||||
<p>
|
||||
|
||||
<!-- ------------------- end of main content --------------- -->
|
||||
|
||||
|
||||
|
||||
Binary file not shown.
@@ -146,18 +146,6 @@
|
||||
"\n",
|
||||
"5. [Journal of Machine Learning Research](http://www.jmlr.org/papers/v19/) \n",
|
||||
"\n",
|
||||
"## Hot Topics Now\n",
|
||||
"\n",
|
||||
"1. Boosting techniques and complex neural networks\n",
|
||||
"\n",
|
||||
"2. [Adversarial examples](https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8)\n",
|
||||
"\n",
|
||||
"3. [Zero shot learning](https://arxiv.org/pdf/1707.00600)\n",
|
||||
"\n",
|
||||
"4. Transfer learning\n",
|
||||
"\n",
|
||||
"5. [Model interpretability](https://christophm.github.io/interpretable-mlbook/interpretability.html)\n",
|
||||
"\n",
|
||||
"## Starting your Machine Learning Project\n",
|
||||
"\n",
|
||||
"1. Identify problem type: classification, generation, regression\n",
|
||||
@@ -249,7 +237,7 @@
|
||||
"\n",
|
||||
"1. [STK2100 Machine learning and statistical methods for prediction and classification](http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html). \n",
|
||||
"\n",
|
||||
"2. [IN3050 Introduction to Artificial Intelligence and Machine Learning](https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html). Introductory course in machine learning and AI with an algorithmic approach. \n",
|
||||
"2. [IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning](https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html). Introductory course in machine learning and AI with an algorithmic approach. \n",
|
||||
"\n",
|
||||
"3. [STK-INF3000/4000 Selected Topics in Data Science](http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html). The course provides insight into selected contemporary relevant topics within Data Science. \n",
|
||||
"\n",
|
||||
@@ -257,13 +245,11 @@
|
||||
"\n",
|
||||
"5. [STK-IN4300 – Statistical learning methods in Data Science](https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html). An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.\n",
|
||||
"\n",
|
||||
"6. [INF4490 Biologically Inspired Computing](http://www.uio.no/studier/emner/matnat/ifi/INF4490/). An introduction to self-adapting methods also called artificial intelligence or machine learning. \n",
|
||||
"6. [IN-STK5000 Adaptive Methods for Data-Based Decision Making](https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html). Methods for adaptive collection and processing of data based on machine learning techniques. \n",
|
||||
"\n",
|
||||
"7. [IN-STK5000 Adaptive Methods for Data-Based Decision Making](https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html). Methods for adaptive collection and processing of data based on machine learning techniques. \n",
|
||||
"7. [IN5400/INF5860 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/). An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.\n",
|
||||
"\n",
|
||||
"8. [IN5400/INF5860 – Machine Learning for Image Analysis](https://www.uio.no/studier/emner/matnat/ifi/IN5400/). An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.\n",
|
||||
"\n",
|
||||
"9. [TEK5040 – Dyp læring for autonome systemer](https://www.uio.no/studier/emner/matnat/its/TEK5040/). The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.\n",
|
||||
"8. [TEK5040 – Dyp læring for autonome systemer](https://www.uio.no/studier/emner/matnat/its/TEK5040/). The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.\n",
|
||||
"\n",
|
||||
"## Additional courses of interest\n",
|
||||
"\n",
|
||||
@@ -271,6 +257,231 @@
|
||||
"\n",
|
||||
"2. [STK4021 Applied Bayesian Analysis and Numerical Methods](https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html)\n",
|
||||
"\n",
|
||||
"## Hot Topics Now\n",
|
||||
"\n",
|
||||
"1. Boosting techniques and complex neural networks\n",
|
||||
"\n",
|
||||
"2. [Adversarial examples](https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8)\n",
|
||||
"\n",
|
||||
"3. [Zero shot learning](https://arxiv.org/pdf/1707.00600)\n",
|
||||
"\n",
|
||||
"4. Transfer learning\n",
|
||||
"\n",
|
||||
"5. [Model interpretability](https://christophm.github.io/interpretable-mlbook/interpretability.html)\n",
|
||||
"\n",
|
||||
"Based on multi-layer nonlinear neural networks, deep learning can\n",
|
||||
"learn directly from raw data, automatically extract and abstract\n",
|
||||
"features from layer to layer, and then achieve the goal of regression,\n",
|
||||
"classification, or ranking. Deep learning has made breakthroughs in\n",
|
||||
"computer vision, speech processing and natural language, and reached\n",
|
||||
"or even surpassed human level. The success of deep learning is mainly\n",
|
||||
"due to the three factors: big data, big model, and big computing.\n",
|
||||
"\n",
|
||||
"In the past few decades, many different architectures of deep neural\n",
|
||||
"networks have been proposed, such as\n",
|
||||
"1. Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;\n",
|
||||
"\n",
|
||||
"2. Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;\n",
|
||||
"\n",
|
||||
"3. Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.\n",
|
||||
"\n",
|
||||
"## Reinforcement Learning\n",
|
||||
"\n",
|
||||
"Reinforcement learning is a sub-area of machine learning. It studies\n",
|
||||
"how agents take actions based on trial and error, so as to maximize\n",
|
||||
"some notion of cumulative reward in a dynamic system or\n",
|
||||
"environment. Due to its generality, the problem has also been studied\n",
|
||||
"in many other disciplines, such as game theory, control theory,\n",
|
||||
"operations research, information theory, multi-agent systems, swarm\n",
|
||||
"intelligence, statistics, and genetic algorithms.\n",
|
||||
"\n",
|
||||
"In March 2016, AlphaGo, a computer program that plays the board game\n",
|
||||
"Go, beat Lee Sedol in a five-game match. This was the first time a\n",
|
||||
"computer Go program had beaten a 9-dan (highest rank) professional\n",
|
||||
"without handicaps. AlphaGo is based on deep convolutional neural\n",
|
||||
"networks and reinforcement learning. AlphaGo’s victory was a major\n",
|
||||
"milestone in artificial intelligence and it has also made\n",
|
||||
"reinforcement learning a hot research area in the field of machine\n",
|
||||
"learning.\n",
|
||||
"\n",
|
||||
"## Transfer learning\n",
|
||||
"\n",
|
||||
"The goal of transfer learning is to transfer the model or knowledge\n",
|
||||
"obtained from a source task to the target task, in order to resolve\n",
|
||||
"the issues of insufficient training data in the target task. The\n",
|
||||
"rationality of doing so lies in that usually the source and target\n",
|
||||
"tasks have inter-correlations, and therefore either the features,\n",
|
||||
"samples, or models in the source task might provide useful information\n",
|
||||
"for us to better solve the target task. Transfer learning is a hot\n",
|
||||
"research topic in recent years, with many problems still waiting to be\n",
|
||||
"solved in this space.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Adversarial learning\n",
|
||||
"\n",
|
||||
"The conventional deep generative model has a potential problem: the\n",
|
||||
"model tends to generate extreme instances to maximize the\n",
|
||||
"probabilistic likelihood, which will hurt its performance. Adversarial\n",
|
||||
"learning utilizes the adversarial behaviors (e.g., generating\n",
|
||||
"adversarial instances or training an adversarial model) to enhance the\n",
|
||||
"robustness of the model and improve the quality of the generated\n",
|
||||
"data. In recent years, one of the most promising unsupervised learning\n",
|
||||
"technologies, generative adversarial networks (GAN), has already been\n",
|
||||
"successfully applied to image, speech, and text.\n",
|
||||
"\n",
|
||||
"## Dual learning\n",
|
||||
"\n",
|
||||
"Dual learning is a new learning paradigm, the basic idea of which is\n",
|
||||
"to use the primal-dual structure between machine learning tasks to\n",
|
||||
"obtain effective feedback/regularization, and guide and strengthen the\n",
|
||||
"learning process, thus reducing the requirement of large-scale labeled\n",
|
||||
"data for deep learning. The idea of dual learning has been applied to\n",
|
||||
"many problems in machine learning, including machine translation,\n",
|
||||
"image style conversion, question answering and generation, image\n",
|
||||
"classification and generation, text classification and generation,\n",
|
||||
"image-to-text, and text-to-image.\n",
|
||||
"\n",
|
||||
"## Distributed machine learning\n",
|
||||
"\n",
|
||||
"Distributed computation will speed up machine learning algorithms,\n",
|
||||
"significantly improve their efficiency, and thus enlarge their\n",
|
||||
"application. When distributed meets machine learning, more than just\n",
|
||||
"implementing the machine learning algorithms in parallel is required.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Meta learning\n",
|
||||
"\n",
|
||||
"Meta learning is an emerging research direction in machine\n",
|
||||
"learning. Roughly speaking, meta learning concerns learning how to\n",
|
||||
"learn, and focuses on the understanding and adaptation of the learning\n",
|
||||
"itself, instead of just completing a specific learning task. That is,\n",
|
||||
"a meta learner needs to be able to evaluate its own learning methods\n",
|
||||
"and adjust its own learning methods according to specific learning\n",
|
||||
"tasks.\n",
|
||||
"\n",
|
||||
"## The Challenges Facing Machine Learning\n",
|
||||
"\n",
|
||||
"While there has been much progress in machine learning, there are also challenges.\n",
|
||||
"\n",
|
||||
"For example, the mainstream machine learning technologies are\n",
|
||||
"black-box approaches, making us concerned about their potential\n",
|
||||
"risks. To tackle this challenge, we may want to make machine learning\n",
|
||||
"more explainable and controllable. As another example, the\n",
|
||||
"computational complexity of machine learning algorithms is usually\n",
|
||||
"very high and we may want to invent lightweight algorithms or\n",
|
||||
"implementations. Furthermore, in many domains such as physics,\n",
|
||||
"chemistry, biology, and social sciences, people usually seek elegantly\n",
|
||||
"simple equations (e.g., the Schrödinger equation) to uncover the\n",
|
||||
"underlying laws behind various phenomena. In the field of machine\n",
|
||||
"learning, can we reveal simple laws instead of designing more complex\n",
|
||||
"models for data fitting? Although there are many challenges, we are\n",
|
||||
"still very optimistic about the future of machine learning. As we look\n",
|
||||
"forward to the future, here are what we think the research hotspots in\n",
|
||||
"the next ten years will be.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Explainable machine learning\n",
|
||||
"\n",
|
||||
"Machine learning, especially deep learning, evolves rapidly. The\n",
|
||||
"ability gap between machine and human on many complex cognitive tasks\n",
|
||||
"becomes narrower and narrower. However, we are still in the very early\n",
|
||||
"stage in terms of explaining why those effective models work and how\n",
|
||||
"they work.\n",
|
||||
"\n",
|
||||
"What is missing: the gap between correlation and causation Most\n",
|
||||
"machine learning techniques, especially the statistical ones, depend\n",
|
||||
"highly on data correlation to make predictions and analyses. In\n",
|
||||
"contrast, rational humans tend to reply on clear and trustworthy\n",
|
||||
"causality relations obtained via logical reasoning on real and clear\n",
|
||||
"facts. It is one of the core goals of explainable machine learning to\n",
|
||||
"transition from solving problems by data correlation to solving\n",
|
||||
"problems by logical reasoning.\n",
|
||||
"\n",
|
||||
"## Quantum machine learning\n",
|
||||
"\n",
|
||||
"Quantum machine learning is an emerging interdisciplinary research\n",
|
||||
"area at the intersection of quantum computing and machine learning.\n",
|
||||
"\n",
|
||||
"Quantum computers use effects such as quantum coherence and quantum\n",
|
||||
"entanglement to process information, which is fundamentally different\n",
|
||||
"from classical computers. Quantum algorithms have surpassed the best\n",
|
||||
"classical algorithms in several problems (e.g., searching for an\n",
|
||||
"unsorted database, inverting a sparse matrix), which we call quantum\n",
|
||||
"acceleration.\n",
|
||||
"\n",
|
||||
"When quantum computing meets machine learning, it can be a mutually\n",
|
||||
"beneficial and reinforcing process, as it allows us to take advantage\n",
|
||||
"of quantum computing to improve the performance of classical machine\n",
|
||||
"learning algorithms. In addition, we can also use the machine learning\n",
|
||||
"algorithms (on classic computers) to analyze and improve quantum\n",
|
||||
"computing systems.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Quantum machine learning algorithms based on linear algebra\n",
|
||||
"\n",
|
||||
"Many quantum machine learning algorithms are based on variants of\n",
|
||||
"quantum algorithms for solving linear equations, which can efficiently\n",
|
||||
"solve N-variable linear equations with complexity of O(log2 N) under\n",
|
||||
"certain conditions. The quantum matrix inversion algorithm can\n",
|
||||
"accelerate many machine learning methods, such as least square linear\n",
|
||||
"regression, least square version of support vector machine, Gaussian\n",
|
||||
"process, and more. The training of these algorithms can be simplified\n",
|
||||
"to solve linear equations. The key bottleneck of this type of quantum\n",
|
||||
"machine learning algorithms is data input—that is, how to initialize\n",
|
||||
"the quantum system with the entire data set. Although efficient\n",
|
||||
"data-input algorithms exist for certain situations, how to efficiently\n",
|
||||
"input data into a quantum system is as yet unknown for most cases.\n",
|
||||
"\n",
|
||||
"## Quantum reinforcement learning\n",
|
||||
"\n",
|
||||
"In quantum reinforcement learning, a quantum agent interacts with the\n",
|
||||
"classical environment to obtain rewards from the environment, so as to\n",
|
||||
"adjust and improve its behavioral strategies. In some cases, it\n",
|
||||
"achieves quantum acceleration by the quantum processing capabilities\n",
|
||||
"of the agent or the possibility of exploring the environment through\n",
|
||||
"quantum superposition. Such algorithms have been proposed in\n",
|
||||
"superconducting circuits and systems of trapped ions.\n",
|
||||
"\n",
|
||||
"## Quantum deep learning\n",
|
||||
"\n",
|
||||
"Dedicated quantum information processors, such as quantum annealers\n",
|
||||
"and programmable photonic circuits, are well suited for building deep\n",
|
||||
"quantum networks. The simplest deep quantum network is the Boltzmann\n",
|
||||
"machine. The classical Boltzmann machine consists of bits with tunable\n",
|
||||
"interactions and is trained by adjusting the interaction of these bits\n",
|
||||
"so that the distribution of its expression conforms to the statistics\n",
|
||||
"of the data. To quantize the Boltzmann machine, the neural network can\n",
|
||||
"simply be represented as a set of interacting quantum spins that\n",
|
||||
"correspond to an adjustable Ising model. Then, by initializing the\n",
|
||||
"input neurons in the Boltzmann machine to a fixed state and allowing\n",
|
||||
"the system to heat up, we can read out the output qubits to get the\n",
|
||||
"result.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Social machine learning\n",
|
||||
"\n",
|
||||
"Machine learning aims to imitate how humans\n",
|
||||
"learn. While we have developed successful machine learning algorithms,\n",
|
||||
"until now we have ignored one important fact: humans are social. Each\n",
|
||||
"of us is one part of the total society and it is difficult for us to\n",
|
||||
"live, learn, and improve ourselves, alone and isolated. Therefore, we\n",
|
||||
"should design machines with social properties. Can we let machines\n",
|
||||
"evolve by imitating human society so as to achieve more effective,\n",
|
||||
"intelligent, interpretable “social machine learning”?\n",
|
||||
"\n",
|
||||
"And much more.\n",
|
||||
"\n",
|
||||
"## The last words?\n",
|
||||
"\n",
|
||||
"Early computer scientist Alan Kay said, **The best way to predict the\n",
|
||||
"future is to create it**. Therefore, all machine learning\n",
|
||||
"practitioners, whether scholars or engineers, professors or students,\n",
|
||||
"need to work together to advance these important research\n",
|
||||
"topics. Together, we will not just predict the future, but create it.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"## Best wishes to you all and thanks so much for your heroic efforts this semester\n",
|
||||
"\n",
|
||||
"<!-- dom:FIGURE: [figures/Nebbdyr2.png, width=500 frac=0.6] -->\n",
|
||||
|
||||
Binary file not shown.
+236
-15
@@ -97,14 +97,6 @@ o _ICLR_: "International Conference on Learning Representations":"https://openre
|
||||
o _ICML_: International Conference on Machine Learning
|
||||
o "Journal of Machine Learning Research":"http://www.jmlr.org/papers/v19/"
|
||||
|
||||
!split
|
||||
===== Hot Topics Now =====
|
||||
|
||||
o Boosting techniques and complex neural networks
|
||||
o "Adversarial examples":"https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8"
|
||||
o "Zero shot learning":"https://arxiv.org/pdf/1707.00600"
|
||||
o Transfer learning
|
||||
o "Model interpretability":"https://christophm.github.io/interpretable-mlbook/interpretability.html"
|
||||
|
||||
|
||||
!split
|
||||
@@ -178,11 +170,10 @@ o Jackknife and many other
|
||||
The link here URL:"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.
|
||||
|
||||
o "STK2100 Machine learning and statistical methods for prediction and classification":"http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html".
|
||||
o "IN3050 Introduction to Artificial Intelligence and Machine Learning":"https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html". Introductory course in machine learning and AI with an algorithmic approach.
|
||||
o "IN3050/IN4050 Introduction to Artificial Intelligence and Machine Learning":"https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html". Introductory course in machine learning and AI with an algorithmic approach.
|
||||
o "STK-INF3000/4000 Selected Topics in Data Science":"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science.
|
||||
o "IN4080 Natural Language Processing":"https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html". Probabilistic and machine learning techniques applied to natural language processing.
|
||||
o "STK-IN4300 – Statistical learning methods in Data Science":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html". An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
|
||||
o "INF4490 Biologically Inspired Computing":"http://www.uio.no/studier/emner/matnat/ifi/INF4490/". An introduction to self-adapting methods also called artificial intelligence or machine learning.
|
||||
o "IN-STK5000 Adaptive Methods for Data-Based Decision Making":"https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html". Methods for adaptive collection and processing of data based on machine learning techniques.
|
||||
o "IN5400/INF5860 – Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/". An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
|
||||
o "TEK5040 – Dyp læring for autonome systemer":"https://www.uio.no/studier/emner/matnat/its/TEK5040/". The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
|
||||
@@ -193,12 +184,242 @@ o "TEK5040 – Dyp læring for autonome systemer":"https://www.uio.no/studier/em
|
||||
o "STK4051 Computational Statistics":"https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html"
|
||||
o "STK4021 Applied Bayesian Analysis and Numerical Methods":"https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html"
|
||||
|
||||
!split
|
||||
===== Hot Topics Now =====
|
||||
|
||||
o Boosting techniques and complex neural networks
|
||||
o "Adversarial examples":"https://medium.com/@ml.at.berkeley/trickingneural-networks-create-your-own-adversarial-examples-a61eb7620fd8"
|
||||
o "Zero shot learning":"https://arxiv.org/pdf/1707.00600"
|
||||
o Transfer learning
|
||||
o "Model interpretability":"https://christophm.github.io/interpretable-mlbook/interpretability.html"
|
||||
|
||||
|
||||
Based on multi-layer nonlinear neural networks, deep learning can
|
||||
learn directly from raw data, automatically extract and abstract
|
||||
features from layer to layer, and then achieve the goal of regression,
|
||||
classification, or ranking. Deep learning has made breakthroughs in
|
||||
computer vision, speech processing and natural language, and reached
|
||||
or even surpassed human level. The success of deep learning is mainly
|
||||
due to the three factors: big data, big model, and big computing.
|
||||
|
||||
In the past few decades, many different architectures of deep neural
|
||||
networks have been proposed, such as
|
||||
o Convolutional neural networks, which are mostly used in image and video data processing, and have also been applied to sequential data such as text processing;
|
||||
o Recurrent neural networks, which can process sequential data of variable length and have been widely used in natural language understanding and speech processing;
|
||||
o Encoder-decoder framework, which is mostly used for image or sequence generation, such as machine translation, text summarization, and image captioning.
|
||||
|
||||
!split
|
||||
===== Reinforcement Learning =====
|
||||
|
||||
Reinforcement learning is a sub-area of machine learning. It studies
|
||||
how agents take actions based on trial and error, so as to maximize
|
||||
some notion of cumulative reward in a dynamic system or
|
||||
environment. Due to its generality, the problem has also been studied
|
||||
in many other disciplines, such as game theory, control theory,
|
||||
operations research, information theory, multi-agent systems, swarm
|
||||
intelligence, statistics, and genetic algorithms.
|
||||
|
||||
In March 2016, AlphaGo, a computer program that plays the board game
|
||||
Go, beat Lee Sedol in a five-game match. This was the first time a
|
||||
computer Go program had beaten a 9-dan (highest rank) professional
|
||||
without handicaps. AlphaGo is based on deep convolutional neural
|
||||
networks and reinforcement learning. AlphaGo’s victory was a major
|
||||
milestone in artificial intelligence and it has also made
|
||||
reinforcement learning a hot research area in the field of machine
|
||||
learning.
|
||||
|
||||
!split
|
||||
===== Transfer learning =====
|
||||
|
||||
The goal of transfer learning is to transfer the model or knowledge
|
||||
obtained from a source task to the target task, in order to resolve
|
||||
the issues of insufficient training data in the target task. The
|
||||
rationality of doing so lies in that usually the source and target
|
||||
tasks have inter-correlations, and therefore either the features,
|
||||
samples, or models in the source task might provide useful information
|
||||
for us to better solve the target task. Transfer learning is a hot
|
||||
research topic in recent years, with many problems still waiting to be
|
||||
solved in this space.
|
||||
|
||||
|
||||
!split
|
||||
===== Adversarial learning =====
|
||||
|
||||
The conventional deep generative model has a potential problem: the
|
||||
model tends to generate extreme instances to maximize the
|
||||
probabilistic likelihood, which will hurt its performance. Adversarial
|
||||
learning utilizes the adversarial behaviors (e.g., generating
|
||||
adversarial instances or training an adversarial model) to enhance the
|
||||
robustness of the model and improve the quality of the generated
|
||||
data. In recent years, one of the most promising unsupervised learning
|
||||
technologies, generative adversarial networks (GAN), has already been
|
||||
successfully applied to image, speech, and text.
|
||||
|
||||
!split
|
||||
===== Dual learning =====
|
||||
|
||||
Dual learning is a new learning paradigm, the basic idea of which is
|
||||
to use the primal-dual structure between machine learning tasks to
|
||||
obtain effective feedback/regularization, and guide and strengthen the
|
||||
learning process, thus reducing the requirement of large-scale labeled
|
||||
data for deep learning. The idea of dual learning has been applied to
|
||||
many problems in machine learning, including machine translation,
|
||||
image style conversion, question answering and generation, image
|
||||
classification and generation, text classification and generation,
|
||||
image-to-text, and text-to-image.
|
||||
|
||||
!split
|
||||
===== Distributed machine learning =====
|
||||
|
||||
Distributed computation will speed up machine learning algorithms,
|
||||
significantly improve their efficiency, and thus enlarge their
|
||||
application. When distributed meets machine learning, more than just
|
||||
implementing the machine learning algorithms in parallel is required.
|
||||
|
||||
|
||||
!split
|
||||
===== Meta learning =====
|
||||
|
||||
Meta learning is an emerging research direction in machine
|
||||
learning. Roughly speaking, meta learning concerns learning how to
|
||||
learn, and focuses on the understanding and adaptation of the learning
|
||||
itself, instead of just completing a specific learning task. That is,
|
||||
a meta learner needs to be able to evaluate its own learning methods
|
||||
and adjust its own learning methods according to specific learning
|
||||
tasks.
|
||||
|
||||
!split
|
||||
===== The Challenges Facing Machine Learning =====
|
||||
|
||||
While there has been much progress in machine learning, there are also challenges.
|
||||
|
||||
For example, the mainstream machine learning technologies are
|
||||
black-box approaches, making us concerned about their potential
|
||||
risks. To tackle this challenge, we may want to make machine learning
|
||||
more explainable and controllable. As another example, the
|
||||
computational complexity of machine learning algorithms is usually
|
||||
very high and we may want to invent lightweight algorithms or
|
||||
implementations. Furthermore, in many domains such as physics,
|
||||
chemistry, biology, and social sciences, people usually seek elegantly
|
||||
simple equations (e.g., the Schrödinger equation) to uncover the
|
||||
underlying laws behind various phenomena. In the field of machine
|
||||
learning, can we reveal simple laws instead of designing more complex
|
||||
models for data fitting? Although there are many challenges, we are
|
||||
still very optimistic about the future of machine learning. As we look
|
||||
forward to the future, here are what we think the research hotspots in
|
||||
the next ten years will be.
|
||||
|
||||
|
||||
!split
|
||||
===== Explainable machine learning =====
|
||||
|
||||
Machine learning, especially deep learning, evolves rapidly. The
|
||||
ability gap between machine and human on many complex cognitive tasks
|
||||
becomes narrower and narrower. However, we are still in the very early
|
||||
stage in terms of explaining why those effective models work and how
|
||||
they work.
|
||||
|
||||
What is missing: the gap between correlation and causation Most
|
||||
machine learning techniques, especially the statistical ones, depend
|
||||
highly on data correlation to make predictions and analyses. In
|
||||
contrast, rational humans tend to reply on clear and trustworthy
|
||||
causality relations obtained via logical reasoning on real and clear
|
||||
facts. It is one of the core goals of explainable machine learning to
|
||||
transition from solving problems by data correlation to solving
|
||||
problems by logical reasoning.
|
||||
|
||||
!split
|
||||
===== Quantum machine learning =====
|
||||
|
||||
Quantum machine learning is an emerging interdisciplinary research
|
||||
area at the intersection of quantum computing and machine learning.
|
||||
|
||||
Quantum computers use effects such as quantum coherence and quantum
|
||||
entanglement to process information, which is fundamentally different
|
||||
from classical computers. Quantum algorithms have surpassed the best
|
||||
classical algorithms in several problems (e.g., searching for an
|
||||
unsorted database, inverting a sparse matrix), which we call quantum
|
||||
acceleration.
|
||||
|
||||
When quantum computing meets machine learning, it can be a mutually
|
||||
beneficial and reinforcing process, as it allows us to take advantage
|
||||
of quantum computing to improve the performance of classical machine
|
||||
learning algorithms. In addition, we can also use the machine learning
|
||||
algorithms (on classic computers) to analyze and improve quantum
|
||||
computing systems.
|
||||
|
||||
|
||||
!split
|
||||
===== Quantum machine learning algorithms based on linear algebra =====
|
||||
|
||||
Many quantum machine learning algorithms are based on variants of
|
||||
quantum algorithms for solving linear equations, which can efficiently
|
||||
solve N-variable linear equations with complexity of O(log2 N) under
|
||||
certain conditions. The quantum matrix inversion algorithm can
|
||||
accelerate many machine learning methods, such as least square linear
|
||||
regression, least square version of support vector machine, Gaussian
|
||||
process, and more. The training of these algorithms can be simplified
|
||||
to solve linear equations. The key bottleneck of this type of quantum
|
||||
machine learning algorithms is data input—that is, how to initialize
|
||||
the quantum system with the entire data set. Although efficient
|
||||
data-input algorithms exist for certain situations, how to efficiently
|
||||
input data into a quantum system is as yet unknown for most cases.
|
||||
|
||||
!split
|
||||
===== Quantum reinforcement learning =====
|
||||
|
||||
In quantum reinforcement learning, a quantum agent interacts with the
|
||||
classical environment to obtain rewards from the environment, so as to
|
||||
adjust and improve its behavioral strategies. In some cases, it
|
||||
achieves quantum acceleration by the quantum processing capabilities
|
||||
of the agent or the possibility of exploring the environment through
|
||||
quantum superposition. Such algorithms have been proposed in
|
||||
superconducting circuits and systems of trapped ions.
|
||||
|
||||
!split
|
||||
===== Quantum deep learning =====
|
||||
|
||||
Dedicated quantum information processors, such as quantum annealers
|
||||
and programmable photonic circuits, are well suited for building deep
|
||||
quantum networks. The simplest deep quantum network is the Boltzmann
|
||||
machine. The classical Boltzmann machine consists of bits with tunable
|
||||
interactions and is trained by adjusting the interaction of these bits
|
||||
so that the distribution of its expression conforms to the statistics
|
||||
of the data. To quantize the Boltzmann machine, the neural network can
|
||||
simply be represented as a set of interacting quantum spins that
|
||||
correspond to an adjustable Ising model. Then, by initializing the
|
||||
input neurons in the Boltzmann machine to a fixed state and allowing
|
||||
the system to heat up, we can read out the output qubits to get the
|
||||
result.
|
||||
|
||||
|
||||
!split
|
||||
===== Social machine learning =====
|
||||
|
||||
Machine learning aims to imitate how humans
|
||||
learn. While we have developed successful machine learning algorithms,
|
||||
until now we have ignored one important fact: humans are social. Each
|
||||
of us is one part of the total society and it is difficult for us to
|
||||
live, learn, and improve ourselves, alone and isolated. Therefore, we
|
||||
should design machines with social properties. Can we let machines
|
||||
evolve by imitating human society so as to achieve more effective,
|
||||
intelligent, interpretable “social machine learning”?
|
||||
|
||||
And much more.
|
||||
|
||||
!split
|
||||
===== The last words? =====
|
||||
|
||||
Early computer scientist Alan Kay said, _The best way to predict the
|
||||
future is to create it_. Therefore, all machine learning
|
||||
practitioners, whether scholars or engineers, professors or students,
|
||||
need to work together to advance these important research
|
||||
topics. Together, we will not just predict the future, but create it.
|
||||
|
||||
|
||||
|
||||
|
||||
!split
|
||||
===== Best wishes to you all and thanks so much for your heroic efforts this semester =====
|
||||
|
||||
FIGURE: [figures/Nebbdyr2.png, width=500 frac=0.6]
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user