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--- start [2017/04/14 08:45]
anderson [April]
+++ start [2017/07/19 20:26]
anderson [August]
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 ====== Schedule ======
-/*
-Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e
-b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]].
-*/
 ===== Announcements =====
-**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 will be available.
-Lecture videos are available at this [[https://echo.colostate.edu/ess/portal/section/a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]].
+/*
+are available at this [[https://echo.colostate.edu/ess/portal/section/a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]].
+*/
-===== January =====
+===== August =====
 |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 1:\\  Jan 17 - Jan 20    | Overview. Intro to machine learning. Python.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/01 Course Overview.ipynb|01 Course Overview]],\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/02 Matrices and Plotting.ipynb|02 Matrices and Plotting]],  | [[http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0|The Great A.I. Awakening]], by Gideon Lewis-Krause, NYT, Dec 14, 2016.\\ Section 1 of   [[http://www.scipy-lectures.org|Scipy Lecture Notes]]      |  |
+| Week 1:\\  Aug 21 - Aug 25    | Course overview\\ What is AI?  Promises and fears.\\ Python review.  |   | [[http://www.nytimes.com/2016/12/14/magazine/the-great-ai-awakening.html?_r=0|The Great A.I. Awakening]], by Gideon Lewis-Krause, NYT, Dec 14, 2016.\\ [[https://www.commondreams.org/news/2017/07/19/fundamental-existential-threat-lawmakers-warned-risks-killer-robots|"Fundamental Existential Threat": Lawmakers Warned of the Risks of Killer Robots]]\\ Section 1 of   [[http://www.scipy-lectures.org|Scipy Lecture Notes]]   |  |
-| Week 2:\\ Jan 23 - Jan 27    | Probability distributions and regression.    | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/03 Linear Regression.ipynb|03 Linear Regression]],\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/04 Gaussian Distributions.ipynb|04 Gaussian Distributions]]    |   |
+| Week 2:\\ Aug 28 - Sept 1    |     |     |   |
-===== February =====
+===== September =====
 |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| 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 3:\\ Sept 4 - Sept 8  |  |  |  |
-| 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 4:\\ Sept 11 - Sept 15   |  |  |  |
-| 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:\\ Sept 18 - Sept 22   |  |  |  |
-| 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 6:\\ Sept 25 - Sept 29   |  |  |  |
-| 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 =====
+===== October =====
 |< 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 7:\\ Oct 2 - Oct 6  |  |  |  |
-| 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 8:\\ Oct 9 - Oct 13  |  |  |  |
-| 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.  |  |
+| Week 9:\\ Oct 16 - Oct 20  |  |  |  |
+| Week 10:\\ Oct 23 - Oct 27  |  |  |  |
-===== April =====
+===== November =====
 |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Apr 3 - Apr 7   | Reinforcement Learning.  Two-player games.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/21 Reinforcement Learning for Two Player Games.ipynb|21 Reinforcement Learning for Two Player Games]]   | [[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, April 5th at 10:00 PM.\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Project Proposal.ipynb|Project Proposal]] due Friday, April 7th at 10:00 PM.  |
+| Week 11:\\ Oct 30 - Nov 3  |   |  |  |
-| Week 12:\\ Apr 10 - Apr 14  | Neural networks as Q functions.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/22 Reinforcement Learning with Neural Network as Q Function.ipynb|22 Reinforcement Learning with Neural Network as Q Function]]\\ [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/17pole.odp|Faster RL by Pre-training]]  |  |  |
+| Week 12:\\ Nov 6 - Nov 10  |  |  |  |
-| Week 13:\\ Apr 17 - Apr 21  |  |  |  |  |
+| Week 13:\\ Nov 13 - Nov 17  |  |  |  |
-| Week 14:\\ Apr 24 - Apr 28  |  |  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5 Control a Marble with Reinforcement Learning.ipynb|A5 Control a Marble with Reinforcement Learning]] due Monday, April 24th at 10:00 PM. |
+|  Nov 20 - Nov 24  |  Fall Break  |
+| Week 14:\\ Nov 27 - Dec 1  |  |  |  |
-===== May =====
+===== December =====
 |< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ May 1 - May 5   |   |  |  |  |
+| Week 15:\\ Dec 4 - Dec 8  |  |  |  |
+| Finals Week:\\ Dec 11 - Dec 15  |   |  |  |

CS440: Introduction to Artificial Intelligence

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