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start [2020/10/27 12:26]
anderson [October]
start [2021/05/28 10:00] (current)
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 ====== Schedule ====== ====== Schedule ======
 +/***
  
 Links to MS Teams Events: Links to MS Teams Events:
   - Lectures: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598126507312?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Tuesdays/Thursdays, 12:30 - 1:45 PM]].   - Lectures: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598126507312?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Tuesdays/Thursdays, 12:30 - 1:45 PM]].
   - Office Hours with Chuck: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598288422204?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Wednesdays, 10:00 - 11:00 AM]]   - Office Hours with Chuck: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598288422204?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Wednesdays, 10:00 - 11:00 AM]]
-  - Office Hours with Dejan: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598541704203?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Mondays, 1:00 - 3:00 PM]] (Starting September 14th)+  - Office Hours with Dejan: [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598541704203?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Mondays, 1:00 - 3:00 PM]] and [[https://teams.microsoft.com/l/meetup-join/19%3a6e74fe18ed0342918877f77c928be0fc%40thread.tacv2/1598541786577?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|Wednesdays, 3:00 - 5:00]]
  
  
 Lecture videos are available from the [[https://colostate.instructure.com/courses/109894|Canvas home page]]. Lecture videos are available from the [[https://colostate.instructure.com/courses/109894|Canvas home page]].
 +
 +***/
  
 To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file: To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
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   export PATH=/usr/local/anaconda/bin:$PATH   export PATH=/usr/local/anaconda/bin:$PATH
  
-This is a tentative schedule of CS545 topics for Fall, 2020.  This will be updated during the summer and as the fall semester continues.+tentative schedule of CS545 topics for Fall, 2021, will appear here during the summer of 2021. 
 + 
 +===== August ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments 
 +| Week 1:\\  Aug 24, 26   | Overview of course, python, machine learning, and expectations of students' understanding of machine learning concepts | 
 +| Week 2:\\  Aug 31, Sept 2  | 
 + 
 +/*** [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/01 Introduction to CS545.ipynb|01 Introduction to CS545]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/02 Searching for Good Weights in a Linear Model.ipynb|02 Searching for Good Weights in a Linear Model]] ***/  | /*** [[http://www.labri.fr/perso/nrougier/from-python-to-numpy/|From Python to Numpy]], Chapters 1 - 2\\ [[http://www.scipy-lectures.org/|Scipy Lectures]], Section 1\\ [[https://jakevdp.github.io/PythonDataScienceHandbook/04.00-introduction-to-matplotlib.html|Visualization with Matplotlib]]\\ [[http://www.deeplearningbook.org/|Deep Learning]], Chapters 1 - 5.1.4  ***/  
 + 
 + 
 +===== September ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments 
 +| Week 3:\\  Sept 7, 9  | 
 +| Week 4:\\  Sept 14, 16  | 
 +| Week 5:\\  Sept 21, 23  | 
 +| Week 6:\\  Sept 28, 30  | 
 + 
 +===== October ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments 
 +| Week 7:\\  Oct 5, 7  | 
 +| Week 8:\\  Oct 12, 14  | 
 +| Week 9:\\  Oct 19, 21  | 
 +| Week 10:\\  Oct 26, 28  | 
 + 
 +===== November ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments 
 +| Week 11:\\  Nov 2, 4  | 
 +| Week 12:\\  Nov 9, 11  | 
 +| Week 13:\\  Nov 16, 18  | 
 +| Nov 23, 25  |  Fall Recess !!  | 
 +| Week 14:\\  Nov 30, Dec 2  | 
 + 
 +===== December ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments 
 +| Week 15:\\  Dec 7, 9  | 
 +| Dec 13-17  |  Final Exams  | 
 + 
 +/***
  
 ===== August ===== ===== August =====
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 | Week 7:\\ Oct 5 - Oct 9\\ <color red>Oct 8 Lecture will not meet, but recording will be available.</color>   | Help with A3. Dimensionality reduction.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 Help with A3.ipynb|10 Help with A3]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/11 Low-Dimensional Representations of Data.ipynb|11 Low-Dimensional Representations of Data]]   | <!-- [[https://www.wired.com/story/ai-pioneer-algorithms-understand-why/|Paper on need for causality]] -->   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3.3 Neural Network Class.ipynb|A3.3 Neural Network Class]] due Monday, Oct 12, 10:00 PM\\ Examples of good solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].| | Week 7:\\ Oct 5 - Oct 9\\ <color red>Oct 8 Lecture will not meet, but recording will be available.</color>   | Help with A3. Dimensionality reduction.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/10 Help with A3.ipynb|10 Help with A3]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/11 Low-Dimensional Representations of Data.ipynb|11 Low-Dimensional Representations of Data]]   | <!-- [[https://www.wired.com/story/ai-pioneer-algorithms-understand-why/|Paper on need for causality]] -->   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3.3 Neural Network Class.ipynb|A3.3 Neural Network Class]] due Monday, Oct 12, 10:00 PM\\ Examples of good solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].|
 | Week 8:\\ Oct 12 - Oct 16    | Brief overview of notes 11.\\ Introduction to Classification  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Classification with Neural Networks.ipynb|12 Classification with Neural Networks]]\\  <!--[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Multilabel Classification.ipynb|12 Multilabel Classification]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13.1 Pytorch nn Module.ipynb|13.1 Pytorch nn Module]] --> |  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3.4 Classification.ipynb|A3.4 Classification]] due Wednesday, Oct 16th, at 10:00 PM --> | | Week 8:\\ Oct 12 - Oct 16    | Brief overview of notes 11.\\ Introduction to Classification  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Classification with Neural Networks.ipynb|12 Classification with Neural Networks]]\\  <!--[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/12 Multilabel Classification.ipynb|12 Multilabel Classification]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13.1 Pytorch nn Module.ipynb|13.1 Pytorch nn Module]] --> |  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A3.4 Classification.ipynb|A3.4 Classification]] due Wednesday, Oct 16th, at 10:00 PM --> |
-| Week 9:\\ Oct 19 - Oct 23    | Convolutional neural networks in numpy.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 NeuralNetwork_Pytorch.ipynb|13 NeuralNetwork_Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Introduction to Convolution.ipynb|14 Introduction to Convolution]] <!-- \\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16 Convolutional Neural Networks in Pytorch.ipynb|16 NeuralNetwork_Pytorch]] <!--[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 NeuralNetwork_Convolutional and CIFAR-10.ipynb|14 NeuralNetwork_Convolutional and CIFAR-10]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Introduction to Reinforcement Learning.ipynb|15 Introduction to Reinforcement Learning]] -->  |  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal.ipynb|Project proposal]] due at 10 pm Wednesday evening, October 21st. --> | +| Week 9:\\ Oct 19 - Oct 23    | Convolutional neural networks in numpy.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/13 NeuralNetwork_Pytorch.ipynb|13 NeuralNetwork_Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 Introduction to Convolution.ipynb|14 Introduction to Convolution]] <!-- \\  <!--[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/14 NeuralNetwork_Convolutional and CIFAR-10.ipynb|14 NeuralNetwork_Convolutional and CIFAR-10]]\\  -->  |  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/Project Proposal.ipynb|Project proposal]] due at 10 pm Wednesday evening, October 21st. --> | 
-| Week 10:\\ Oct 26 - Oct 30   | Fully-connected and Convolutional Neural Nets in Pytorch and Tensorflow  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Convolutional Neural Networks.ipynb|15 Convolutional Neural Networks]]  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16 Reinforcement Learning with Neural Network as Q Function.ipynb|16 Reinforcement Learning with Neural Network as Q Function]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Reinforcement Learning to Control a Marble.ipynb|17 Reinforcement Learning to Control a Marble]]  --> [[http://incompleteideas.net/book/the-book.html|Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto, 2nd edition | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4.1 Neural Network Classifier.ipynb|A4.1 Neural Network Classifier]] due Tuesday Oct 27, at 10:00 PM  |+| Week 10:\\ Oct 26 - Oct 30   | Fully-connected and Convolutional Neural Nets in Pytorch  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/15 Convolutional Neural Networks.ipynb|15 Convolutional Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16.1 Convolutional Neural Networks in Pytorch.ipynb|16.1 Convolutional Neural Networks in Pytorch]]  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/16 Reinforcement Learning with Neural Network as Q Function.ipynb|16 Reinforcement Learning with Neural Network as Q Function]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Reinforcement Learning to Control a Marble.ipynb|17 Reinforcement Learning to Control a Marble]]  -->  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A4.1 Neural Network Classifier.ipynb|A4.1 Neural Network Classifier]] due Tuesday Oct 27, at 10:00 PM\\ Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].  |
  
 ===== November ===== ===== November =====
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 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Nov 2 - Nov 6  | Introduction to Reinforcement Learning. Deep Reinforcement Learning  | |   A5  | +| Week 11:\\ Nov 2 - Nov 6  | Comparing network performance. Introduction to Reinforcement Learning. Deep Reinforcement Learning [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/17 Partitioning Data to Compare Neural Network Performance.ipynb|17 Partitioning Data to Compare Neural Network Performance]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Introduction to Reinforcement Learning.ipynb|18 Introduction to Reinforcement Learning]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Reinforcement Learning with Neural Network as Q Function.ipynb|19 Reinforcement Learning with Neural Network as Q Function]]   | [[http://incompleteideas.net/book/the-book.html|Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto, 2nd edition  |    |  
-| Week 12:\\ November 11 15    Transfer learning in Reinforcement Learning  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Embedding With Conv1d.ipynb|18 Embedding With Conv1d.ipynb]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Embedding Network.ipynb|19 Embedding Network]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Transformer Tutorial.ipynb|20 Transformer Tutorial]] -->  | <!-- [[https://towardsdatascience.com/how-to-code-the-transformer-in-pytorch-24db27c8f9ec|How to Code the Transformer in Pytorch]] by Samuel Lynn-Evans --> +| Week 12:\\ November 13    Deep reinforcement learning on simulated physical control problem.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Reinforcement Learning to Control a Marble.ipynb|20 Reinforcement Learning to Control a Marble]]  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/18 Embedding With Conv1d.ipynb|18 Embedding With Conv1d.ipynb]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/19 Embedding Network.ipynb|19 Embedding Network]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/20 Transformer Tutorial.ipynb|20 Transformer Tutorial]] -->  | <!-- [[https://towardsdatascience.com/how-to-code-the-transformer-in-pytorch-24db27c8f9ec|How to Code the Transformer in Pytorch]] by Samuel Lynn-Evans -->  |   
-| Week 13:\\ Nov 16 - Nov 20   Natural Language Processing   |  |  A6  |+| Week 13:\\ Nov 16 - Nov 20       [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A5 Neural Networks in Pytorch.ipynb|A5 Neural Networks in Pytorch]] due Wednesday, Nov 18 at 10:00 PM\\  Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].  |   |
 | Nov 23 - Nov 27  |  Fall Recess!  |  |   | | Nov 23 - Nov 27  |  Fall Recess!  |  |   |
  
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 |< 100% 18% 20% 22% 20% 20%  >| |< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 14:\\ Nov 30 - Dec 4     +| Week 14:\\ Nov 30 - Dec 4   Clustering.\\ Support Vector Machines.   [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/22 K-Means Clustering, K-Nearest-Neighbor Classification.ipynb|22 K-Means Clustering, K-Nearest-Neighbor Classification]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/23 Support Vector Machines.ipynb|23 Support Vector Machines]]    
-| Week 15:\\ Dec 7 - Dec 11    |  | +| Week 15:\\ Dec 7 - Dec 11   Transfer learning in Reinforcement Learning. Brain-computer interfaces.  
-| Finals Week:\\ Dec 14 - Dec 18  |  |  |  |   A7 due Tuesday, Dec 15th   |+| Finals Week:\\ Dec 14 - Dec 18  |  |  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A6.2 Reinforcement Learning to Control a Robot.ipynb|A6.2 Reinforcement Learning to Control a Robot]]  due Tuesday, Dec 15th, 10:00 PM. Here is [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/A3mysolution.tar|A3mysolution.tar]], a neural network implementation you may choose to use for A6.\\ Good examples of solutions are [[https://www.cs.colostate.edu/~anderson/cs545/notebooks/goodones|available here]].   | 
  
 +***/
  
start.1603823189.txt.gz · Last modified: 2020/10/27 12:26 by anderson