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 ====== Schedule ====== ====== Schedule ======
 +/***
  
 Links to MS Teams Events: Links to MS Teams Events:
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 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 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 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 9 - 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 12:\\ November 9 - 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      |  |  | [[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   |+| 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   | Clustering   | [[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]]    | +| 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  |  |  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs545/notebooks/A6 Reinforcement Learning to Control a Robot.ipynb|A6 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.   |+| 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.1606928132.txt.gz · Last modified: 2020/12/02 09:55 by 127.0.0.1