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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.

August

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

September

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

October

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

November

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

December

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.
start.txt · Last modified: 2018/06/05 13:10 (external edit)