CS440: Introduction to Artificial Intelligence

User Tools

Site Tools

Trace:
schedule

Differences

This shows you the differences between two versions of the page.

Link to this comparison view

Both sides previous revision Previous revision
Next revision 		Previous revision
Next revision Both sides next revision
schedule [2016/01/12 14:49]
anderson 		schedule [2020/08/27 13:54]
anderson [Announcements]
Line 1: Line 1:
 ====== Schedule ====== ====== Schedule ======
  
-== January ==+===== Announcements =====
  
 +The live lecture is in the MS Teams meeting [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598126257845?context=%7b%22Tid%22%3a%22afb58802-ff7a-4bb1-ab21-367ff2ecfc8b%22%2c%22Oid%22%3a%22bcd6d782-40c2-430e-8091-fd9ebd260de7%22%7d|at this link]].
 +
 +Lecture and office hour videos are available from the Home page of our 
 +[[https://colostate.instructure.com/courses/109411|Canvas site]].
 +
 +To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
 +
 +  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.
 +
 +
 +===== August =====
 +
 +|< 100% 18% 20% 22% 20% 20%  >|
 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^
-| Week 1: Jan 19 - Jan 22    | Overview.     | [[http://nbviewer.ipython.org/url/www.cs.colostate.edu/~anderson/cs480/notebooks/01 Course Overview.ipynb|01 Course Overview]] |  Chapter 1 of textbook. 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: Jan 25 - Jan 29    |      |       |+
  
-== February ==+===== September =====
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ +|< 100% 18% 20% 22% 20% 20%  >| 
-| Week 3: Feb 1 - Feb 5    |      |       | +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^nderson/cs545/doku.php?id=schedule#september 
-| Week 4: Feb 8 - Feb 12    |      |       | +| 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 5: Feb 15 - Feb 19    |      |       | +| 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 Tuesday, Sept. 8, 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: Feb 22 - Feb 26    |      |       |+| 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 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. -->   |
  
-== March ==+===== October =====
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ +|< 100% 18% 20% 22% 20% 20%  >| 
-| Week 7: Feb 29 - Mar 5    |      |       | +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
-| Week 8: Mar 7 - Mar 11    |      |       | +| 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]]  |  | 
-|  Mar 14 - Mar 18    |  Spring Break!    |       | +| 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: Mar 21 - Mar 25    |      |       | +| 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: Mar 28 - Apr 1    |      |       |+| 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 ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
 +| 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 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.\\ -->   | 
 +| 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 23 - Nov 27  |  Fall Recess!  | 
 + 
 +===== December ===== 
 + 
 +|< 100% 18% 20% 22% 20% 20%  >| 
 +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ 
 +| 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.  -->  |  
 +| Week 15:\\ Dec 7 - Dec 11  |   | <!-- **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  |   |  | |   |
  
-== April == 
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ 
-| Week 11: Apr 4 - Apr 8    |      |       | 
-| Week 12: Apr 11 - Apr 15    |      |       | 
-| Week 13: Apr 18 - Apr 22    |      |       | 
-| Week 14: Apr 25 - Apr 29    |      |       | 
  
-== May == 
  
-^ Week      ^ Topic      ^ Reading          ^ Assignments  ^ 
-| Week 15: May 2 - May 6    |      |       | 
  
schedule.txt · Last modified: 2024/01/08 18:40 (external edit)

Page Tools

Except where otherwise noted, content on this wiki is licensed under the following license: CC Attribution-Noncommercial-Share Alike 4.0 International
CC Attribution-Noncommercial-Share Alike 4.0 International Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki