CS440: Introduction to Artificial Intelligence

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start [2020/08/03 12:08]
anderson [December] 		start [2020/08/28 08:50]
anderson [Announcements]
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 ===== Announcements ===== ===== Announcements =====
  
 +Links to live MS Teams events:
 +- Lectures: Tuesday/Thursday [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598126257845?|Tuesdays/Thursdays, 2:00 - 3:15 PM]]
 +- Office hours: Apoorv [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598126257845?|Mondays, 2:00 - 4:00 PM]]
 +- Office hours: Chaitanya [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598301087268?|Fridays, 2:00 - 4:00 PM]]
 +- Office hours: Chuck [[https://teams.microsoft.com/l/meetup-join/19%3a323d2d59a8f64282b836e440b8cb32d9%40thread.tacv2/1598288070646?|Wednesdays, 9:00 - 10:00 AM]]
 + 
  
-Lecture videos are available from the Canvas site (in the menu on the left) by selecting +Recordings of lecture and office hour videos are available from the Home page of our  
-[[https://colostate.instructure.com/courses/109411|Echo 360]].+[[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: To use jupyter notebooks on our CS department machines, you must add this line to your .bashrc file:
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-^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ +^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^nderson/cs545/doku.php?id=schedule#september 
-| 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 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 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 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 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 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 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  |
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 ^  Week      ^  Topic      ^  Material  ^  Reading          ^  Assignments  ^ ^  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 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.\\ --> [[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 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]]  |  | | 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!  | |  Nov 23 - Nov 27  |  Fall Recess!  |
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 | 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 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//  -->  |   |  | 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  |   |  | | 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.   |+| Final Exam Week:\\ Dec 14 - Dec 18  |   |  | |   |
  
  
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