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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.
Week 4:
Sept 14 - Sept 18
A* optimality, admissible heuristics, effective branching factor.
Local search and optimization.
09 Heuristic Functions
10 Local Search
Chapter 4 A2.1 Iterative-Deepening Search due Tuesday, Sept. 15, 10:00 PM.
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.
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
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