CS440: Introduction to Artificial Intelligence

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start [2017/11/14 08:08]
anderson [November] 		start [2020/07/22 16:35]
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 ===== Announcements ===== ===== Announcements =====
  
-Sept 7:  Assignment 2 is now complete. 
  
-Aug 31: Assignment 1 now includes another example. +Lecture videos are available from the Canvas site (in the menu on the left) by selecting 
- +[[https://colostate.instructure.com/courses/109411|Echo 360]].
- +
-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]].+
  
 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 CS440 topics for Fall, 2020.  This will be updated during the summer and as the fall semester continues.
-are available at this [[https://echo.colostate.edu/ess/portal/section/a5759ae3-82dc-43df-b515-dd944a6c4976|CS480 video recordings site]]. +
-*/+
  
  
 ===== August ===== ===== August =====
  
-|< 100% 10% 20% 30% 20% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| 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 1:\\  Aug 24 - Aug 28    | 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]]   | Chapters 1, 2, 3.1 of Russell and Norvig.\\ Section 1 of [[http://www.scipy-lectures.org|Scipy Lecture Notes]]  \\ [[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\\ <!-- [[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\\ -->    |  | 
-| 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  |   |  +
  
 ===== September ===== ===== September =====
  
-|< 100% 10% 20% 30% 20% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| 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 2:\\ Aug 31 - Sept 4    | 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/03 Problem-Solving Agents.ipynb|03 Problem-Solving Agents]]\\  [[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 of Russell and Norvig  |   |  
-| 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 3:\\ Sept 7 - Sept 11  | Informed search. A* search. Python classes, sorting, numpy arrays.  |   | Rest of Chapter 3  |  <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A1 Uninformed Search.ipynb|A1 Uninformed Search]] due Friday, Sept. 7, 10:00 PM.  Submit your notebook in Canvas.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  --> | 
-| 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 4:\\ Sept 14 - Sept 18   | A* optimality, admissible heuristics, effective branching factor.\\ Local search and optimization.  | <!-- [[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]]\\ [[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 Friday, Sept. 14, 10:00 PM.  Submit your notebook in Canvas.\\ Here are [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/goodones|good solutions from your classmates]]  -->  | 
-| 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.   |+| Week 5:\\ Sept 21 - Sept 25   | 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 6:\\ Sept 28 - Oct 2   | Negamax, with pruning. Introduction to Reinforcement Learning.  | <!-- [[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/14 Introduction to Reinforcement Learning.ipynb|14 Introduction to Reinforcement Learning]]   -->    | Chapter 21\\ [[http://incompleteideas.net/book/bookdraft2017nov5.pdf|Reinforcement Learning: An Introduction]]   |  <!--  [[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 Wednesday, Sept. 26, 10:00 PM.  Submit your notebook in Canvas. -->   |
  
 ===== October ===== ===== October =====
  
-|< 100% 10% 20% 30% 20% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  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 7:\\ Oct 5 - Oct 9  | Reinforcement Learning for Two-Player Games.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/14 Introduction to Reinforcement Learning.ipynb|14 Introduction to Reinforcement Learning]]\\ [[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]]  -->  | Chapter 21\\ [[http://incompleteideas.net/book/bookdraft2017nov5.pdf|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 8:\\ Oct 12 - Oct 16  | Introduction to Neural Networks  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/16 Introduction to Neural Networks.ipynb|16 Introduction to 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]]  -->  | Sections 18.6 and 18.7  |   | 
-| 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 9:\\ Oct 19 - Oct 23  | More Neural Networks. Autoencoders.  | <!-- [[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]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/22 Autoencoder Neural Networks.ipynb|22 Autoencoder 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.  |+| Week 10:\\ Oct 26 - Oct 30  | 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/A4 Reinforcement Learning Solution to Towers of Hanoi.ipynb|A4 Reinforcement Learning Solution to Towers of Hanoi]] due Monday, Oct. 22, 10:00 PM.  Submit your notebook in Canvas. -->  |  |
  
 ===== November ===== ===== November =====
  
-|< 100% 10% 20% 30% 20% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| 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 11:\\ Nov 2 - Nov 6  | Classification with Neural Networks. Reinforcement Learning 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/21 Reinforcement Learning with a Neural Network as the Q Function.ipynb|21 Reinforcement Learning with a Neural Network as the Q Function]]  -->  | |   | 
-| 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 12:\\ Nov 9 - Nov 13  | Introduction to Pytorch.\\ Constraint satisfaction.\\ Min-conflicts.  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/23 Introduction to Pytorch.ipynb|23 Introduction to Pytorch]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/24 Constraint Satisfaction Problems.ipynb|24 Constraint Satisfaction Problems]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/25 Min-Conflicts in Python with Examples.ipynb|25 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]]  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A5 Neural Networks.ipynb|A5 Neural Networks]] due Monday, Nov. 5, 10:00 PM.\\ --> [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/Project Proposal.ipynb|Project Proposal]] due Friday, November 13th, at 10:00 PM.   | 
-| Week 13:\\ Nov 13 - Nov 17  | Faster Reinforcement Learning  |  |  | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Neural Networks.ipynb|A6 Neural Networks]] due Wednesday, November 15th, at 10:00 PM.  | +| Week 13:\\ Nov 16 - Nov 20  | Natural language understanding and translation.   | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/26 Natural Language.ipynb|26 Natural Language]]\\ [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/27 Word Embeddings.ipynb|27 Word Embeddings]] -->  | [[https://towardsdatascience.com/word-embedding-with-word2vec-and-fasttext-a209c1d3e12c|Word2Vec and FastText Word Embedding with Gensim]]  |  | 
-|  Nov 20 - Nov 24  |  Fall Break  | +|  Nov 23 - Nov 27  |  Fall Recess!  |
-| Week 14:\\ Nov 27 - Dec 1  | Constraint satisfaction. Min-conflicts  |  | 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]]  | +
  
 ===== December ===== ===== December =====
  
-|< 100% 10% 20% 30% 20% 20%  >|+|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 15:\\ Dec 4 - Dec 8  |   |  |   |  +| Week 14:\\ Nov 30 - Dec 4  | Faster Reinforcement Learning   | <!-- [[http://www.cs.colostate.edu/~anderson/cs440/notebooks/15ijcnn.pdf|Slides for Faster Reinforcement Learning After Pretraining]] -->   | [[http://www.cs.colostate.edu/~anderson/res/rl/pretrainijcnn15.pdf|Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics]] by Anderson, Lee and Elliott  | <!-- [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/A6 Min-Conflicts.ipynb|A6 Min-Conflicts]] due Wednesday, Nov. 28, 10:00 PM.  -->  |  
-| Finals Week:\\ Dec 11 - Dec 15  |   |  | | Final Project notebook is due Tuesday, Dec 12th, 10:00 pm.  |+| Week 15:\\ Dec 7 - Dec 11  | Voluntary in-class project presentations.  | <!-- **Dec 3:**\\ Tom Cavey: //Image Classification and Object Detection of Things Around CSU//\\ Jason Stock: //Classification of Data from the Sloan Digital Sky Survey//\\ Marylou Nash: //Physical Routing on ICs or PCBs with A*//\\  **Dec 5:**\\ Jake Walker: //Legal, Ethical, and Security Concerns for Autonomous Driving Technologies//\\ Andy Dolan: //Using Machine Learning Methods to Classify BGP Messages//\\ Miles Wood: //Using Q-Learning to Learn to Play Chad, a Chess Variant//\\ Apoorv Pandey: //Using Q-Learning to Learn to Play 2x2 Dots and Boxes//\\ **Dec 7:**\\ Markus Dabell: //Classification of Handwritten Digits from the MNIST Dataset//\\ Sajeeb Roy Chowdhury: //Searching for Optimal Schreier Trees in the Field of Combinatorics//\\ Mike Hamilton: //The Amazon AWS DeepRacer Platform for Reinforcement Learning Experimentation//  -->  |   |  
 +| Final Exam Week:\\ Dec 14 - Dec 18  |   |  | | Final Project notebook is due Tuesday, Dec 15th, 10:00 pm. Here is an [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs440/notebooks/Example of Project Report.ipynb|notebook explaining what is expected]] for your final report.   | 
 + 
  
  
  
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