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Saint Louis University

Computer Science 362
Artificial Intelligence

Michael Goldwasser

Fall 2013

Dept. of Math & Computer Science


Please note that the schedule for future classes is tentative.

Also note: For each lecture, we denote both a topic as well as the associated reading in the textbook. The live link on the 'topic' leads to a page of my personal notes for that lecture. Those personal notes are not nearly as complete or polished as the associated readings from the text. In truth, they exist mostly to provide a brief outline of the day's lecture. Students are expected to read the associated sections of the text. In cases where my notes include material which is not explicitly in the text, I will add the word "notes" to the explicit reading list.

Week Day Topic Reading
Aug 27, Aug 29 Tues Course administration,
Introduction to AI
Ch. 1
Thur Intro. to Propositional Logic Ch. 2.1-2.3
(my notes)
Sep 3, Sep 5 Tues CNF, Resolution Ch. 2.4
(my notes)
Thur Horn Clauses, Backward Chaining Ch. 2.5
(my notes)
Sep 10, Sep 12 Tues First-Order Predicate Logic Ch. 3
(my notes)
Thur Limits of Computation
Introduction to Prolog
Ch. 4
Ch. 5
(my notes)
Sep 17, Sep 19 Tues more Prolog
(arithmetic, lists)
(my notes)
Thur more Prolog
(output, cuts, examples)
(my notes)
Sep 24, Sep 26 Tues In-class work on asgn03 asgn03
Thur Search Algorithms: Uniformed Search Ch. 6
(my notes)
Oct 1, Oct 3 Tues Search Algorithms: Heuristic Search Ch. 6
(my notes)
Thur Two-Player Games
Minimax Search
Alpha-Beta Pruning
Ch. 6.4, 6.5
(my demos)
(another demo)
Oct 8, Oct 10 Tues Alpha-beta with heuristics
Move ordering
Other practical considerations
Thur Discussion of Pente code and asgn04 asgn04
Oct 15, Oct 17 Tues Exam Review
Thur Midterm Exam
(includes material through Oct 3 - info )
Oct 22, Oct 24 Tues No Class: Fall Break
Thur Discrete Probability Ch. 7.1, 7.2
(my notes)
Oct 29, Oct 31 Tues Bayesian Networks Ch. 7.4
(my notes)
Thur Intro. to Machine Learning
Superverised Learning
Perceptron Algorithm
(my notes)
Nov 5, Nov 7 Tues Naive Bayesian classification
Thur k-Nearest Neighbor classification,
Decision Tree classification
Nov 12, Nov 14 Tues Introduction to asgn06
Thur Continued work on asgn06
Nov 19, Nov 21 Tues Introduction to Artificial Neural Networks Ch. 8
Thur Hopfield Networks (notes and software)
Nov 26, Nov 28 Tues
Thur No Class: Thanksgiving
Dec 3, Dec 5 Tues

Dec 17 Tues Final Exam (2:00-3:50)

Michael Goldwasser
CSCI 362, Fall 2013
Last modified: Thursday, 21 November 2013
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