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Links to live MS Teams events:
Recordings of lecture and office hour videos are available from the Home page of our 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.
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 1: Aug 24 - Aug 28 | What is AI? Promises and fears. Python review. Problem-Solving Agents. | 01 Introduction to AI 02 Introduction to Python | Chapters 1, 2, 3.1 of Russell and Norvig. Section 1 of Scipy Lecture Notes AI, People, and Society, by Eric Horvitz. Automated Ethics, by Tom Chatfield. The Great A.I. Awakening, by Gideon Lewis-Krause |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 2: Aug 31 - Sept 4 | Help with A1. Problem-solving search and how to measure performance. Iterative deepening and other uninformed search methods. | 03 Problem-Solving Agents 04 Measuring Search Performance 05 Iterative Deepening and Other Uninformed Search Methods | Sections 3.1 - 3.4 of Russell and Norvig | |
Week 3: Sept 7 - Sept 11 | Informed search. A* search. Python classes, sorting, numpy arrays. | 06 Python Implementation of Iterative Deepening 07 Informed Search 08 Python Classes | Rest of Chapter 3 | A1.1 Uninformed Search due Tuesday, Sept. 8, 10:00 PM. Submit your notebook in Canvas. Here are good solutions from your classmates |
Week 4: Sept 14 - Sept 18 | A* optimality, admissible heuristics | 09 Heuristic Functions 10 Local Search | Chapter 4 | A2.1 Iterative-Deepening Search due Tuesday, Sept. 15, 10:00 PM. Here are good solutions from your classmates |
Week 5: Sept 21 - Sept 25 | Effective branching factor. Local search and optimization. Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. | 11 Adversarial Search | Chapter 5 | |
Week 6: Sept 28 - Oct 2 | Negamax, with pruning. Introduction to Reinforcement Learning. | 12 Negamax 13 Modern Game Playing 14 Introduction to Reinforcement Learning | Chapter 21 Reinforcement Learning: An Introduction | A3 A*, IDS, and Effective Branching Factor due Wednesday, Sept. 30, 10:00 PM. Here are good solutions from your classmates |
Week | Topic | Material | Reading | Assignments | |
---|---|---|---|---|---|
Week 7: Oct 5 - Oct 9 Oct 8 Lecture will not meet, but recording will be available. | Reinforcement Learning for Two-Player Games. | 15 Reinforcement Learning for Two-Player Games | Chapter 21 Reinforcement Learning: An Introduction | ||
Week 8: Oct 12 - Oct 16 | Constraint satisfaction. Min-conflicts. | 16 Constraint Satisfaction Problems 17 Min-Conflicts | Chapter 6 | ||
Week 9: Oct 19 - Oct 23 | Natural language understanding and translation. | Word2Vec and FastText Word Embedding with Gensim | A4 Reinforcement Learning Solution To Towers of Hanoi due Tuesday, Oct. 20, 10:00 PM. | ||
Week 10: Oct 26 - Oct 30 | Introduction to Neural Networks | Sections 18.6 and 18.7 | A5 due Oct 30, 10:00 PM |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 11: Nov 2 - Nov 6 | More Neural Networks. Autoencoders. | |||
Week 12: Nov 9 - Nov 13 | Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression. | A6 due Tuesday Nov 10, 10:00 PM | ||
Week 13: Nov 16 - Nov 20 | Classification with Neural Networks. Reinforcement Learning with Neural Networks. | A7 due Thursday Nov 19, 10:00 PM | ||
Nov 23 - Nov 27 | Fall Recess! |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 14: Nov 30 - Dec 4 | Reinforcement Learning with Neural Networks | Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics by Anderson, Lee and Elliott | ||
Week 15: Dec 7 - Dec 11 | Recent AI Success | |||
Final Exam Week: Dec 14 - Dec 18 | No exam. | Final assignment A8 due Dec 15th. |