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--- schedule [2016/05/17 10:30]
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+++ schedule [2017/12/19 16:31]
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 ====== Schedule ======
-Follow this link to view all [[https://echo.colostate.edu/ess/portal/section/37e115b6-e68b-4318-89ff-d1ecf025c0b9|lecture videos]].
 ===== Announcements =====
-**May 9:** At the bottom of this page is a link to a summary of the content expected in your project reports.
+Sept 7:  Assignment 2 is now complete.
-**April 29:** My latest neural network code is available at [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/nn7.tar|nn7.tar]].
+Aug 31: Assignment 1 now includes another example.
-===== January =====
-|< 100% 20% 20% 30% 10% 20%  >|
+Lecture videos are available from the Canvas site (in the menu on the left) by selecting [[https://colostate.instructure.com/courses/55296/external_tools/2755|Echo 360]].
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 1:\\  Jan 19 - Jan 22    | 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]],  | Text: Sections 1.1-1.5. Section 1 of   [[http://www.scipy-lectures.org|Scipy Lecture Notes]]      |  |
-| Week 2:\\ Jan 25 - Jan 29    | 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]],\\ [[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]]    | Sections 4.1-4.2, 4.6-4.9, 5.8-5.9     |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Linear Regression.ipynb|A1 Linear Regression]] due Friday, January 29th at 10:00 PM. Download and unzip [[http://www.cs.colostate.edu/~anderson/cs480/notebooks/A1 Grader.zip|A1 Grader.zip]]\\ Here are five examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1a.ipynb|A1a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1b.ipynb|A1b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1c.ipynb|A1c]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1d.ipynb|A1d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A1good/A1e.ipynb|A1e]]   |
-===== February =====
+To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
-|< 100% 20% 20% 30% 10% 20%  >|
+  export PATH=/usr/local/anaconda/bin:$PATH
-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 3:\\ Feb 1 - Feb 5      | Ridge regression. Data partitioning. On-line, incremental regression. Regression with fixed nonlinearities.  | [[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/09 Linear Regression with Fixed Nonlinear Features.ipynb|09 Linear Regression with Fixed Nonlinear Features]]    | |
-| Week 4:\\ Feb 8 - Feb 12     | Nonlinear regression with 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]]  | 11.1-11.5, 11.7.1, 11.7.4, 11.8.1-11.8.2  |
-| Week 5:\\ Feb 15 - Feb 19    | Autoencoders. Recurrent neural networks.     | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/12 Autoencoder Neural Networks.ipynb|12 Autoencoder Neural Networks]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/13 Recurrent Neural Networks.ipynb|13 Recurrent Neural Networks]]   | 11.9, 11.12, 11.14   |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2 Linear Regression with Fixed Nonlinear Features.ipynb|A2 Linear Regression with Fixed Nonlinear Features]] due Monday, Feb 15 at 10:00 PM.\\ Here are three examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2good/A2a.ipynb|A2a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2good/A2b.ipynb|A2b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A2good/A2c.ipynb|A2c]]   |
-| Week 6:\\ Feb 22 - Feb 26    | Classification, generative models.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/14 Introduction to Classification.ipynb|14 Introduction to Classification]]   | 4.3-4.5, 5.5-5.7  |
-===== March =====
+/*
+are available at this [[https://echo.colostate.edu/ess/portal/section/a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]].
+*/
-|< 100% 20% 20% 30% 10% 20%  >|
+===== August =====
+|< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 7:\\ Feb 29 - Mar 5     | Classification, Introduction to Support Vector Machines.   | Monday: GTA Jake Lee will discuss questions on Assignment 3.  Wednesday: Guest lecture by Dr. Asa Ben-Hur.\\ [[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://www.cs.colostate.edu/~anderson/cs480/notebooks/svms-asa.pdf|SVM Slides]]  | 10.1-10.4, 10.5-10.10    | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3 Neural Network Regression.ipynb|A3 Neural Network Regression]] due Monday, Feb 29 at 10:00 PM.\\ Here are examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3a.ipynb|A3a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3b.ipynb|A3b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3c.ipynb|A3c]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3d.ipynb|A3d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3e.ipynb|A3e]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3f.ipynb|A3f]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3g.ipynb|A3g]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A3good/A3h.ipynb|A3h]]  |
+| Week 1:\\  Aug 21 - Aug 25    | What is AI?  Promises and fears.\\ Python review.\\ Problem-Solving Agents.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/01 Introduction to AI.ipynb|01 Introduction to AI]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/02 Introduction to Python.ipynb|02 Introduction to Python]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/03 Problem-Solving Agents.ipynb|03 Problem-Solving Agents]]   | Chapters 1, 2, 3.1.\\ [[http://science.sciencemag.org/content/357/6346/7.full|AI, People, and Society]], by Eric Horvitz.\\ [[https://aeon.co/essays/can-we-design-machines-to-make-ethical-decisions|Automated Ethics]], by Tom Chatfield.\\ [[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]], by Julia Conley\\ Section 1 of [[http://www.scipy-lectures.org|Scipy Lecture Notes]]   |  |
-| Week 8:\\ Mar 7 - Mar 11     | Classification with neural networks.     | [[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]]  | 11.7.2     |
+| Week 2:\\ Aug 28 - Sept 1    | Problem-solving search and how to measure performance.\\ Iterative deepening and other uninformed search methods.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/04 Measuring Search Performance.ipynb|04 Measuring Search Performance]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/05 Iterative Deepening and Other Uninformed Search Methods.ipynb|05 Iterative Deepening and Other Uninformed Search Methods]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/06 Python Implementation of Iterative Deepening.ipynb|06 Python Implementation of Iterative Deepening]]    | Sections 3.1 - 3.4  |   |
-|  Mar 14 - Mar 18    | Spring Break!    |       |
-| Week 9:\\ Mar 21 - Mar 25    | Bottleneck, and deep networks.    | [[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]]  | 11.8.3, 11.11, 11.13     |
-| Week 10:\\ Mar 28 - Apr 1    | Convolutional neural nets. Clustering.  | [[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 Clustering.ipynb|20 Clustering]] \\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/21 Mixtures of Gaussians.ipynb|21 Mixtures of Gaussians]]   | 7.1-7.10  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4 Classification with LDA, QDA, and Logistic Regression.ipynb|A4 Classification with LDA, QDA, and Logistic Regression]] due Tuesday, March 29 at 10:00 PM. Here are examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4a.ipynb|a4a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4b.ipynb|a4b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4c.ipynb|a4c]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4d.ipynb|a4d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4e.ipynb|a4e]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4f.ipynb|a4f]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4g.ipynb|a4g]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A4good/a4h.ipynb|a4h]]  |
-===== April =====
-|< 100% 20% 20% 30% 10% 20%  >|
+===== September =====
+|< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 11:\\ Apr 4 - Apr 8      | Reinforcement Learning  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/22 Introduction to Reinforcement Learning.ipynb|22 Introduction to Reinforcement Learning]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/23 Reinforcement Learning for Two Player Games.ipynb|23 Reinforcement Learning for Two Player Games]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/24 Reinforcement Learning with Neural Network as Q Function.ipynb|24 Reinforcement Learning with Neural Network as Q Function]]  | 18.1-18.9  |
+| Week 3:\\ Sept 4 - Sept 8  | Informed search. A* search. Python classes, sorting, numpy arrays.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/07 Informed Search.ipynb|07 Informed Search]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/08 Python Classes.ipynb|08 Python Classes]]  | Rest of Chapter 3  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A1 Uninformed Search.ipynb|A1 Uninformed Search]] due Tuesday, September 5th, at 10:00 PM.\\ Here are examples of good A1 notebooks: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/goodones/A1-good-a.ipynb|a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/goodones/A1-good-b.ipynb|b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/goodones/A1-good-c.ipynb|c]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/goodones/A1-good-d.ipynb|d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/goodones/A1-good-e.ipynb|e]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/goodones/A1-good-f.ipynb|f]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/goodones/A1-good-g.ipynb|g]]  |
-| Week 12:\\ Apr 11 - Apr 15    | Dimensionality reduction.  |  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/25 Tic-Tac-Toe with Neural Network Q Function.ipynb|25 Tic-Tac-Toe with Neural Network Q Function]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/26 Linear Dimensionality Reduction.ipynb|26 Linear Dimensionality Reduction]]\\  [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/27 Nonlinear Dimensionality Reduction with Digits Example.ipynb|27 Nonlinear Dimensionality Reduction with Digits Example]]  | 6.1-6.8, 6.10-6.13  |
+| Week 4:\\ Sept 11 - Sept 15   | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization.  |[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/09 Heuristic Functions.ipynb|09 Heuristic Functions]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/10 Local Search.ipynb|10 Local Search]]  | Chapter 4  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A2 Iterative-Deepening Search.ipynb|A2 Iterative-Deepening Search]] due Thursday, September 14th, at 10:00 PM.\\ [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/A2answer.tar|A2answer.tar]]   |
-| Week 13:\\ Apr 18 - Apr 22    | Nonparametric methods  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/28 Nonparametric Classification with K Nearest Neighbors.ipynb|28 Nonparametric Classification with K Nearest Neighbors]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/29 Support Vector Machines.ipynb|29 Support Vector Machines]]  | 8.1-8.10  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5 Reinforcement Learning Solution to Visual Tic-Tac-Toe.ipynb|A5 Reinforcement Learning Solution to Visual Tic-Tac-Toe]] due Wednesday, April 20 at 10:00 PM.\\ Here are examples of good solutions: [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/a.ipynb|a5a]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/b.ipynb|a5b]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/c.ipynb|a5c]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/d.ipynb|a5d]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/e.ipynb|a5e]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/f.ipynb|a5f]],[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/A5good/g.ipynb|a5g]]\\  Check in your [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Project Proposal.ipynb|Project Proposal]] by Friday, April 22nd, at 10:00 PM  |
+| Week 5:\\ Sept 18 - Sept 22   | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/11 Adversarial Search.ipynb|11 Adversarial Search]] | Chapter 5  |
-| Week 14:\\ Apr 25 - Apr 29    | | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/31 Machine Learning for Brain-Computer Interfaces.ipynb|31 Machine Learning for Brain-Computer Interfaces]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/32 Comparison of Algorithms for BCI.ipynb|32 Comparison of Algorithms for BCI]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/33 Convolutional Neural Networks for BCI.ipynb|33 Convolutional Neural Networks for BCI]]  | |
+| Week 6:\\ Sept 25 - Sept 29   | Negamax, with pruning. | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/12 Negamax.ipynb|12 Negamax]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/13 Modern Game Playing.ipynb|13 Modern Game Playing]]    |   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A3 A*, IDS, and Effective Branching Factor.ipynb|A3 A*, IDS, and Effective Branching Factor]] due Friday, September 29th, at 10:00 PM.   |
+===== October =====
+|< 100% 10% 20% 30% 20% 20%  >|
+^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+| Week 7:\\ Oct 2 - Oct 6  | Introduction to Reinforcement Learning.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/14 Introduction to Reinforcement Learning.ipynb|14 Introduction to Reinforcement Learning]]   | Chapter 21\\ [[http://incompleteideas.net/sutton/book/the-book-2nd.html|Reinforcement Learning: An Introduction]]  |  |
+| Week 8:\\ Oct 9 - Oct 13  | Reinforcement Learning for Two-Player Games.\\ Introduction to Neural Networks  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/15 Reinforcement Learning for Two-Player Games.ipynb|15 Reinforcement Learning for Two-Player Games]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/16 Introduction to Neural Networks.ipynb|16 Introduction to Neural Networks]]  | Sections 18.6 and 18.7  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A4 Negamax with Alpha-Beta Pruning and Iterative Deepening.ipynb|A4 Negamax with Alpha-Beta Pruning and Iterative Deepening]] due Wednesday, October 11th, at 10:00 PM.  |
+| Week 9:\\ Oct 16 - Oct 20  | More Neural Networks  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/17 More Introduction to Neural Networks.ipynb|17 More Introduction to Neural Networks]]  |
+| Week 10:\\ Oct 23 - Oct 27  | Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/18 Introduction to Classification.ipynb|18 Introduction to Classification]]   |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A5 Reinforcement Learning Solution to Towers of Hanoi.ipynb|A5 Reinforcement Learning Solution to Towers of Hanoi]] due Wednesday, October 25th, at 10:00 PM.  |
-===== May =====
+===== November =====
-|< 100% 20% 20% 30% 10% 20%  >|
+|< 100% 10% 20% 30% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ May 2 - May 6    | Multiple models.\\ PLEASE ATTEND MAY 6th LECTURE TO FILL OUT THE ASCSU STUDENT COURSE SURVEYS! Distance-section students will be filling out the survey on-line.   | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/34 Ensembles of Convolutional Neural Networks.ipynb|34 Ensembles of Convolutional Neural Networks]], [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/35 Ensembles of Convolutional Neural Networks for BCI.ipynb|35 Ensembles of Convolutional Neural Networks for BCI]]  | 17.1-17.12   |
+| Week 11:\\ Oct 30 - Nov 3  | Classification with Neural Networks  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/19 Classification with Linear Logistic Regression.ipynb|19 Classification with Linear Logistic Regression]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/20 Classification with Nonlinear Logistic Regression Using Neural Networks.ipynb|20 Classification with Nonlinear Logistic Regression Using Neural Networks]]  | |[[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/Project Proposal.ipynb|Project Proposal]] due Wednesday, November 1st, at 10:00 PM. |
+| Week 12:\\ Nov 6 - Nov 10  | Reinforcement Learning with Neural Networks.\\ Lecture and Chuck's office hours on Thursday are <color red>cancelled</color>.  He will be out of town.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/21 Reinforcement Learning with a Neural Network as the Q Function.ipynb|21 Reinforcement Learning with a Neural Network as the Q Function]]  |  |
+| Week 13:\\ Nov 13 - Nov 17  | Faster Reinforcement Learning. Autoencoder neural networks.  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/22 Autoencoder Neural Networks.ipynb|22 Autoencoder Neural Networks]]  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Neural Networks.ipynb|A6 Neural Networks]] due <color red>Friday, November 17th, at 10:00 PM.</color>  |
+|  Nov 20 - Nov 24  |  Fall Break  |
+| Week 14:\\ Nov 27 - Dec 1  | Constraint satisfaction. Min-conflicts  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/23 Constraint Satisfaction Problems.ipynb|23 Constraint Satisfaction Problems]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/24 Min-Conflicts in Python with Examples.ipynb|24 Min-Conflicts in Python with Examples]]  | Chapter 6.\\ [[http://dl.acm.org/citation.cfm?id=1928809|A new iterated local search algorithm for solving broadcast scheduling problems in packet radio networks]]  |
-| Week 16:\\ May 10    | Final Project Notebook Due.    | | | Check in final project notebook by Tuesday, May 10th, at 10:00 PM. [[Final Project Report|Here is a summary]] of what is expected in your reportsl  |
+===== December =====
+|< 100% 10% 20% 30% 20% 20%  >|
+^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
+| Week 15:\\ Dec 4 - Dec 8  | Recurrent neural networks and use in natural language\\ <color red>Dec 7, Thursday, PLEASE ATTEND. Course Surveys will be filled out.</color>  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Natural Language.ipynb|25 Natural Language]] |   |
+| Finals Week:\\ Dec 11 - Dec 15  |   |  | | Final Project notebook is due Tuesday, Dec 12th, 10:00 pm. [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/Example of Project Report.ipynb|Here is a simple example.]]\\ Here is a [[|list of links to almost everyone's final reports]]  |
-Selected Project Reports (in no particular order):
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/dayalex_67206_4742841_Day FinalProject.ipynb|Tracking GLR-1 Receptors in Time-Lapse Imaging]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/torresandy_23458_4746802_Torres AFinal.ipynb|Kaggle's Santander Data Analysis and Classification]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Calderon Jaramillo Report.ipynb|Reinforcement Learning in Pong]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/mishraankit_38357_4746332_Mishra_Final_Project-2.ipynb|Evaluating factors affecting pricing in California Markets utilizing Machine Learning methods. ]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/arkenbergadrion_483_4743731_Arkenberg Project.ipynb|Classifying Raptor Feathers Using Convolutional Neural Networks (CNNs)]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/EasonFinalProject.ipynb|Exploring Reddit Karma Trends]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/porecasey_52217_4747688_cpore_final_project-1.ipynb|A Comparison of Classifiers on Digitally Captured Handwritten Digits]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Yang Project.ipynb|Applying Neural Network Regrssion on BMP Database]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Connect-4 with Neural Network Q Function.ipynb|Connect-4 with Neural Network Q Function]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Douda Final Report.ipynb|Modeling Red and White Wine Quality From Multiple Factors]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Torain Project.ipynb|Trying to determine Customer Satisfaction based on numeric variables]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/iyerrohit_34532_4748004_Final_Project.ipynb|Sentiment Analysis on Amazon Product Reviews]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Dale Final Project.ipynb|Estimating Income Based Off Socioeconomic Factors]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Edwards Final Project.ipynb|Clustering of Genomic Functional Elements]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Herman Final Project.ipynb|Predicting NBA Team Wins]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/larisonjosh_52678_4747696_Larison Final Project.ipynb|Applying Logistic Regression to EEG P300 Waves]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/ODell Project.ipynb|Prediction of Stock Market, in Near Future]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/rustjonathan_20568_4744501_Rust Term Project-1.ipynb|Predicting the Next Pitch in Baseball]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/vanettenjeff_60636_4747400_VanEtten project-1.ipynb|Is there a relationship between atmospheric CO2 levels and/or sunspots and hurricane activity in the Atlantic Ocean?]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Project Report Follmer.ipynb|Analyzing StarCraft Players and Game Details]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Kurth finalProject.ipynb|Neural Network Modeling of NFL data to predict Fantasy Football defense]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Strand Project.ipynb|Simplified New View Synthesis with Convolutional Neural Networks]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Lakin_FinalProject.ipynb|Neural Networks using Theano for the Data Mining Cup 2016]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/benfrajmohtadi_98144_4747591_BenFraj Project.ipynb|Expedia Hotel Recommendations]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Kerry McKean Final Project Report.ipynb|Neural Network Regression for Musical Prediction]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/FinalReportCarbonariRyan.ipynb|Comparison of Classification Techniques to Detect the Higgs Boson]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/myergreg_54762_4744409_Final Myer.ipynb|Predicting Ionosphere Severity Using A Neural Network]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/whitehillnick_39527_4746200_Whitehill Final Report.ipynb|Ultimate Tic Tac Toe]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Olson Project.ipynb|Predicting NFL QB Passing Yards]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Sharma Project.ipynb|Neural Network vs Linear Regression analysis on the Dow Jones Industrial Average]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Hamor Project.ipynb|Reinforcement Learning with Neural Network Q Function]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Szejna Final.ipynb|Multilabel classification of emotions in music using Neural Networks]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Diede Final Project Report.ipynb|Predicting the Geographic Origin of Ethnic Traditional Music with Supervised Machine Learning]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Haiming Final Project.ipynb|Tensorflow Versus Our Neural Network Implementation]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/martstyler_36730_4747946_Marts Final Project.ipynb|Machine Learning and Sports Analytics in the NHL]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/xuliyan_59738_4748165_Xu Project (1).ipynb|Simplified Go Game with Neural Network Q Function]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Tao final project.ipynb|The k-means optimizing algorithm]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/alsharifmuhammad_100260_4748143_Alsharif_Project.ipynb|Learning to play Pong using Reinforcement Learning]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Christensen Final Project.ipynb|Reinforcement Learning for Solving the Traveling Salesman Problem]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Purandare Report.ipynb|Wholesale Customers Data Classification]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Overton Project.ipynb|Machine Learning Applied to Control Systems]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Baby_Weight_Prediction.ipynb|Prediction of Child's Birth Weight]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/ReillyFinal.ipynb|Tensorflow for Reinforcement Learning]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Shaffer Final.ipynb|K Top Synonyms]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Project_text.ipynb|Predicting Shelter Animal Outcomes with Different Machine Learning Algorithms]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Larson Final.ipynb|Predicting NFL Scores by Weather]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Shah & Sudalaikkan Term Project.ipynb|Outlier Detection using Mahalanobis’ distance]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/Xu Mitra Final Project Submission.ipynb|Training Flappy Bird Using Q-Learning]]
-  * [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/hudavid_52971_4747929_Hu&Zhu FinalProjectPart1-2.ipynb|Finding the best training setting for GOMOKU for two levels of given system resources, Part 1]] and [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/Projects/hudavid_52971_4747930_Hu&Zhu FinalProjectPart2-2.ipynb|Part 2]]

CS440: Introduction to Artificial Intelligence

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