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 ===== Announcements =====
-**Feb 19:** The links in A3 for nnA3.tar and A3grader.tar now work.
+**March 20:** A4grader.tar linked to on the A4 web page has been updated. It longer checks for QDA-related functions.
+**March 18:** There will be no lecture class on Wednesday, March 22nd.  Chuck's office hours on March 22nd are cancelled.
 Lecture videos are available at this [[https://echo.colostate.edu/ess/portal/section/a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]].
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 | Week 3:\\ Jan 30 - Feb 3      | Probabilistic Linear Regression. Ridge regression. Data partitioning. On-line, incremental regression.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/05 Fitting Gaussians.ipynb|05 Fitting Gaussians]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/06 Probabilistic Linear Regression.ipynb|06 Probabilistic Linear Regression]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/07 Linear Ridge Regression and Data Partitioning.ipynb|07 Linear Ridge Regression and Data Partitioning]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/08 Sample-by-Sample Linear Regression.ipynb|08 Sample-by-Sample Linear Regression]]    | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Linear Regression.ipynb|A1 Linear Regression]] due Monday, January 30th at 10:00 PM.   |
 | Week 4:\\ Feb 6 - Feb 10   | Regression with fixed nonlinearities. Nonlinear regression with neural networks.\\ Feb 10: Guest Speaker [[https://www.linkedin.com/in/mike-morain-07223710|Mike Morain]], Machine Learning at Amazon, UK.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/09 Linear Regression with Fixed Nonlinear Features.ipynb|09 Linear Regression with Fixed Nonlinear Features]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]]   | |   |
-| Week 5:\\ Feb 13 - Feb 17   | Neural Networks  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]],\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/11 More Nonlinear Regression with Neural Networks.ipynb|11 More Nonlinear Regression with Neural Networks]]   | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2 Ridge Regression with K-Fold Cross-Validation.ipynb|A2 Ridge Regression with K-Fold Cross-Validation]] due Monday, February 13th at 10:00 PM.\\ Here are [[A2-good-ones | examples of good A2 reports.]]  |
+| Week 5:\\ Feb 13 - Feb 17   | Neural Networks  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/10 Nonlinear Regression with Neural Networks.ipynb|10 Nonlinear Regression with Neural Networks]],\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/11 More Nonlinear Regression with Neural Networks.ipynb|11 More Nonlinear Regression with Neural Networks]]   | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2 Ridge Regression with K-Fold Cross-Validation.ipynb|A2 Ridge Regression with K-Fold Cross-Validation]] due Monday, February 13th at 10:00 PM.\\ Here are [[A2-good-ones|examples of good A2 reports.]]  |
 | Week 6:\\ Feb 20 - Feb 24   | Neural Networks. Autoencoders. Guest lectures by our GTA, Jake Lee.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/12 Autoencoder Neural Networks.ipynb|12 Autoencoder Neural Networks]]   | |   |
-| Week 7:\\ Feb 27 - Mar 3   | Recurrent Neural Networks.  | \\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/14 Introduction to Classification.ipynb|14 Introduction to Classification]]   | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Wednesday, March 1st at 10:00 PM.  |
+| Week 7:\\ Feb 27 - Mar 3   | Recurrent Neural Networks.\\ Conditional probabilities and Bayes Rule  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/14 Introduction to Classification.ipynb|14 Introduction to Classification]]   | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Wednesday, March 1st at 10:00 PM.\\ Here are [[A3-good-ones|examples of good A3 reports.]]  |
+===== March =====
+|< 100% 10% 20% 30% 20% 20%  >|
+^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+| Week 8:\\ Mar 6 - Mar 10   | Classification. LDA and QDA. Linear and Nonlinear Logistic Regression.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/15 Classification with Linear Logistic Regression.ipynb|15 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/16 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|16 Classification with Nonlinear Logistic Regression Using Neural Networks]]   | |  |
+| Week 9:\\ Mar 20, Mar 24\\ <color red>No class March 22nd.</color>  | Classification. Analysis of Trained Networks. Bottleneck Networks. Hand-Drawn Digit Classification.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/17 Analysis of Neural Network Classifiers and Bottleneck Networks.ipynb|17 Analysis of Neural Network Classifiers and Bottleneck Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/18 Digits.ipynb|18 Digits]]  |  |  |
+| Week 10:\\ Mar 27 - Mar 31  | Convolutional Neural Networks. Reinforcement Learning.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/19 Convolutional Neural Networks.ipynb|19 Convolutional Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/20 Introduction to Reinforcement Learning.ipynb|20 Introduction to Reinforcement Learning]]  | [[http://incompleteideas.net/sutton/book/the-book-2nd.html| Reinforcement Learning: An Introduction]], by Richard Sutton and Andrew Barto. 2nd edition draft. On-line and free.  |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4 Classification with LDA and Logistic Regression.ipynb|A4 Classification with LDA and Logistic Regression]] due Wednesday, March 29th at 10:00 PM.\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Project Proposal.ipynb|Project Proposal]] due Friday, March 31st at 10:00 PM.  |
+===== April =====
+|< 100% 10% 20% 30% 20% 20%  >|
+^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+| Week 11:\\ Apr 3 - Apr 7   |   |  |  |  |
+| Week 12:\\ Apr 10 - Apr 14  |  |  |  |  |
+| Week 13:\\ Apr 17 - Apr 21  |  |  |  |  |
+| Week 14:\\ Apr 24 - Apr 28  |  |  |  |  |
+===== May =====
+|< 100% 10% 20% 30% 20% 20%  >|
+^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+| Week 15:\\ May 1 - May 5   |   |  |  |  |

CS440: Introduction to Artificial Intelligence

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