15381 Artificial Intelligence:

This class is all about autonomy: how can machines make their own decisions and make them well? The goal is that by the end of the class you will be an expert in a wide range of useful technologies for automated decision making. You'll have seen practical examples of putting AI into commercial, scientific, federal and consumer applications. And hopefully you'll be ready to find new AI technologies and applications.
We will not stick strictly to the traditional definitions of AI, but will cover technologies from a wider range of disciplines that your instructor believes are most important for practical autonomous decisionmaking.
Class lectures: Tuesdays & Thursdays 1.30pm2:50pm, Wean Hall 7500 starting on Tuesday January 16th, 2007
Instructors:
Note: if you find the doors in NSH or EDSH are locked after 5pm, please contact the TAs via phone so that we can open the door for you. Class Assistant:

Mailing Lists:
Textbook:
Other resources:
Grading:
Policy on late homework:
Homework must be handed in at the start of class (1.30pm) on the due date.
Policy on collaboration:
You are encouraged to discuss the general algorithms and ideas in the class in order to help each other answer homework questions. You are also welcome to give each other examples that are not on the assignment in order to demonstrate how to solve problems. But we require you to
This policy is in order to be fair to the rest of the students in the class. We will have a grading policy of watching for cheating and we will follow up if it is detected. Some assignments will allow you to form teams of two people. In that case you will submit one paper on behalf of the partnership, and partners may explicitly help each other out with answers, but the above anticopying rules still apply between teams.
Day & Time: 
Tuesday 6:00pm8:00pm 
Location : 
WeH 4623 
Date: 
Announced in class and on the website 
There will be occasional optional review sessions on Tuesday 6:00pm8:00pm in
WeH 4623, often used for things like Q&A for assignments and reviewing
material for exams. Please check the website for information on upcoming review
sessions.
Assignments will be available for downloading on the specified dates. Please contact the grader listed for a particular question if you have concerns regarding the scoring of that question.

Links 
Out 
Return 
Solutions 
HW1 
Tue Jan. 23 
Tue Feb. 6 
Homework 1 solutions Graders 

HW2 
hw2.pdf 
Tue Feb. 6 
Tue Feb. 20 
Homework 2 solutions Graders 
HW3 
hw3.pdf 
Tue Feb. 20 
Tue Mar. 20 
Homework 3 grade distribution Graders 
HW4 
Tue Mar. 20 
Tue Apr. 3 
Homework 4
Solutions Homework 4 grade distribution Graders 

HW5 
Tue Apr. 3 
Tue Apr. 17 
Homework 5 grade distribution Graders 

HW6 
Tue Apr. 17 
Tue May 1 
Graders 
Date 
Topic 
Chapter 
Notes 
Links/Slides 





Jan. 16 
Intro 




SEARCH 



Jan. 18 
Search 
3 


Jan. 23 
Search 
3 
HW1 out 

Jan. 25 
Search: Hill Climbing, Stochastic Search, Simulated Annealing 
3,4 


Jan. 30 
Search: Hill Climbing, Stochastic Search, Simulated Annealing 
3,4 
HW1 review 

Feb. 1 
Constraint Satisfaction Problems 
5 


Feb. 6 
Constraint Satisfaction Problems 
5 
HW1 due; HW2 out 

Feb. 8 
Robot Motion Planning 
25 



GAME THEORY 



Feb. 13 
Algorithms for Playing and Solving Games 
6 
HW2 review 

Feb. 15 
Games with Hidden Information 
6 

Feb. 20 
NonZeroSum Games 
6 
HW2 due; HW3 out 

Feb. 22 
Game Theory, continued 
6 


Feb. 27 
Auctions and Negotiations 
6 
HW3 review 


SYMBOLIC REASONING 



Mar. 1 
Automated Theorem Proving with Propositional Logic 
8,9 

Mar. 6 
Reasoning, Cont. 
11 
Midterm review 

Mar. 8 
Midterm Exam 

Midterm 
Previous Exams:
S06sols Solutions: S07sols Graders 
Mar. 13/15 
Midterm Break 




PROBABILISTIC REASONING 



Mar. 20 
Probability and Uncertainty 
HW3 due; HW4 out 

Mar. 22 
Probability and Uncertainty 

Probability and Uncertainty 2 slides 

Mar. 27 
Bayes Nets 
14 
HW4 review 

Mar. 29 
Bayes Nets 
14 

Apr. 3 
Markov Decision Processes 
16,17 
HW4 due; HW5 out 

Apr. 5 
Markov Decision Processes 
16,17 



LEARNING 



Apr. 10 
Intro + Decision Trees 
18 
HW5 review 

Apr. 12 
Decision Trees (cont.) 
18 

Apr. 17 
Probabilistic Learning and Naive Bayes 
20 
HW5 due; HW6 out 

Apr. 19 
No class (carnival) 


Apr. 24 
Neural Networks 
20 


Apr. 26 
Clustering and Cross Validation 
14 


May 1 
Reinforcement Learning 
21 
HW6 due 

May 3 
Reinforcement Learning 
21 


May 11 
Final Exam 

05:30pm08:30pm 
Previous Exams:
S05sols Solutions: 