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This is a tentative schedule of CS440 topics for Fall, 2018. This will be updated during the summer and as the fall semester continues.
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 1: Aug 20 - Aug 24 | What is AI? Promises and fears. Python review. Problem-Solving Agents. | 01 Introduction to AI 02 Introduction to Python 03 Problem-Solving Agents | Chapters 1, 2, 3.1. AI, People, and Society, by Eric Horvitz. Automated Ethics, by Tom Chatfield. The Great A.I. Awakening, by Gideon Lewis-Krause, NYT, Dec 14, 2016. "Fundamental Existential Threat": Lawmakers Warned of the Risks of Killer Robots, by Julia Conley Section 1 of Scipy Lecture Notes | |
Week 2: Aug 27 - Aug 31 | Problem-solving search and how to measure performance. Iterative deepening and other uninformed search methods. | 04 Measuring Search Performance 05 Iterative Deepening and Other Uninformed Search Methods 06 Python Implementation of Iterative Deepening | Sections 3.1 - 3.4 |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 3: Sept 4 - Sept 7 No class on the Sept 3 University Holiday | Informed search. A* search. Python classes, sorting, numpy arrays. | 07 Informed Search 08 Python Classes | Rest of Chapter 3 | |
Week 4: Sept 10 - Sept 14 | A* optimality, admissible heuristics, effective branching factor. Local search and optimization. | 09 Heuristic Functions 10 Local Search | Chapter 4 | |
Week 5: Sept 17 - Sept 21 | Adversarial search. Minimax. Alpha-beta pruning. Stochastic games. | 11 Adversarial Search | Chapter 5 | |
Week 6: Sept 24 - Sept 28 | Negamax, with pruning. | 12 Negamax 13 Modern Game Playing |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 7: Oct 2 - Oct 6 | Introduction to Reinforcement Learning. | 14 Introduction to Reinforcement Learning | Chapter 21 Reinforcement Learning: An Introduction | |
Week 8: Oct 9 - Oct 13 | Reinforcement Learning for Two-Player Games. Introduction to Neural Networks | 15 Reinforcement Learning for Two-Player Games 16 Introduction to Neural Networks | Sections 18.6 and 18.7 | |
Week 9: Oct 16 - Oct 20 | More Neural Networks | 17 More Introduction to Neural Networks | ||
Week 10: Oct 23 - Oct 27 | Introduction to Classification. Bayes Rule. Generative versus Discriminative. Linear Logistic Regression. | 18 Introduction to Classification |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 11: Oct 30 - Nov 2 | Classification with Neural Networks | 19 Classification with Linear Logistic Regression 20 Classification with Nonlinear Logistic Regression Using Neural Networks | Project Proposal due Wednesday, Oct 31st, at 10:00 PM. | |
Week 12: Nov 5 - Nov 9 | Reinforcement Learning with Neural Networks. | 21 Reinforcement Learning with a Neural Network as the Q Function | ||
Week 13: Nov 12 - Nov 16 | Faster Reinforcement Learning. Autoencoder neural networks. | 22 Autoencoder Neural Networks | ||
Nov 19 - Nov 23 | Fall Recess | |||
Week 14: Nov 26 - Nov 30 | Constraint satisfaction. Min-conflicts | 23 Constraint Satisfaction Problems 24 Min-Conflicts in Python with Examples | Chapter 6. A new iterated local search algorithm for solving broadcast scheduling problems in packet radio networks |
Week | Topic | Material | Reading | Assignments |
---|---|---|---|---|
Week 15: Dec 3 - Dec 7 | Recurrent neural networks and use in natural language | 25 Natural Language | ||
Final Exam Week: Dec 10 - Dec 14 | Final Project notebook is due Tuesday, Dec 11th, 10:00 pm. |