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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 | Problem-solving search and how to measure performance. Iterative deepening and other uninformed search methods. | 03 Problem-Solving Agents 04 Measuring Search Performance | 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 | A1.1 Uninformed Search due Tuesday, Sept. 8, 10:00 PM. Submit your notebook in Canvas. | |
Week 4: Sept 14 - Sept 18 | A* optimality, admissible heuristics, effective branching factor. Local search and optimization. | Chapter 4 | ||
Week 5: Sept 21 - Sept 25 | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. | Chapter 5 | ||
Week 6: Sept 28 - Oct 2 | Negamax, with pruning. Introduction to Reinforcement Learning. | Chapter 21 Reinforcement Learning: An Introduction |
Week | Topic | Material | Reading | Assignments | |
---|---|---|---|---|---|
Week 7: Oct 5 - Oct 9 | Reinforcement Learning for Two-Player Games. | Chapter 21 Reinforcement Learning: An Introduction | |||
Week 8: Oct 12 - Oct 16 | Introduction to Neural Networks | Sections 18.6 and 18.7 | |||
Week 9: Oct 19 - Oct 23 | More Neural Networks. Autoencoders. | ||||
Week 10: Oct 26 - Oct 30 | Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression. |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 11: Nov 2 - Nov 6 | Classification with Neural Networks. Reinforcement Learning with Neural Networks. | |||
Week 12: Nov 9 - Nov 13 | Introduction to Pytorch. Constraint satisfaction. Min-conflicts. | Chapter 6 A new iterated local search algorithm for solving broadcast scheduling problems in packet radio networks | ||
Week 13: Nov 16 - Nov 20 | Natural language understanding and translation. | Word2Vec and FastText Word Embedding with Gensim | ||
Nov 23 - Nov 27 | Fall Recess! |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 14: Nov 30 - Dec 4 | Faster Reinforcement Learning | Faster Reinforcement Learning After Pretraining Deep Networks to Predict State Dynamics by Anderson, Lee and Elliott | ||
Week 15: Dec 7 - Dec 11 | ||||
Final Exam Week: Dec 14 - Dec 18 |