Saint Louis University 
Computer Science 362

Dept. of Math & Computer Science 
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 
syllabus
Ch. 1 
Thur  Intro. to Propositional Logic 
Ch. 2.12.3
(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  FirstOrder 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  Inclass 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 
TwoPlayer Games
Minimax Search AlphaBeta Pruning 
Ch. 6.4, 6.5
(my demos) (another demo) 

Oct 8, Oct 10  Tues 
Alphabeta 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 
kNearest 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  
Thur  
Dec 17  Tues  Final Exam (2:003:50)  