Date 
Topic 
Required Reading 
Notes 
Assignments Out 
Slides 
8/23 
Introduction, Agents 
ch 1 



8/28 
State Space Search 
ch 3.14 (2e: ch 3) 

Project 1, due 9/13 

8/30 
Heuristic Search 
ch 3.56 (2e: ch 4.13) 



9/4 
Local Search 
ch 4.1 

Think of two problems that could be used with a genetic algorithm framework. The first problem
should make crossover easy (minimal conflicts between sections of the parents being combined) and the
second problem should make crossover more difficult. Be ready to discuss on Thursday 9/6. 

9/6 
Constraint Satisfaction Problems 
ch 6.16.4 

Homework 1, due 9/20. 

9/11 
Adversarial Search 
ch 5.15.3 



9/13 
A* Project Questions 




9/18 
Adversarial Search II 
ch 5.4 

Project 2, due 10/2, 11:59pm 

9/20 
Probability crash course 
ch 13 



9/25 
Probability crash course II 




9/27 
Bayes Nets I 
ch 14.114.2 

Homework 2, due 10/9 in class, in hard copy. 

10/2 
Bayes Nets II 
ch 14.4 



10/4 
Bayes Nets III 
ch 14.414.5 



10/9 
Review for midterm 




10/11 
Midterm 




10/16 
Midterm debriefing, Learning 
18.1, 18.2 



10/18 
The Naive Bayes model 
(not well covered in book, see slides) 



10/23 
Hypothesis testing, maximum likelihood, maximum a posteriori, combining evidence using (assumed) conditional independence 
(not well covered in book, see slides) 


slides 
10/25 
Learning Naive Bayes classifiers (spam classification) 
(not well covered, see slides) 

Project 3, due 11/13 11:59pm on Moodle. 
slides 
10/30 
Markov models I 
15.1 



11/1 
No class; professor ill 




11/6 
Markov models II 
15.215.3 



11/8 
Markov models III 
15.215.3 

Homework 3, due 11/20 in class, in hard copy. 

11/13 
Reinforcement Learning I 
21.121.2 



11/15 
Reinforcement Learning II: Value Iteration 




11/20 
Reinforcement Learning III: Value Iteration and Qlearning 




11/27 
Reinforcement Learning IV: Qlearning 


Project 4, due 12/5 11:59pm on Moodle. 

11/29 
Practice with Qlearning 




12/4 
Wrapup 



slides 