Steam-powered Turing Machine University of Washington Department of Computer Science & Engineering
CSE 573 - Artificial Intelligence - Spring 2012
Monday, Wednesday 12-1:20 PM in CSE 403
  CSE Home  About Us    Search    Contact Info 

Instructor: Mausam
(mausam at cs dot washington dot edu)
Office hours: by appointment, CSE 454
TA: Janara Christensen
(janara at cs dot washington dot edu)
Office hours: Wednesdays 3-4, CSE 610 (cancelled 4/25 and 5/2)


Week Dates Topics & Lecture Notes Readings Supplementary Resources Advanced Resources
1 Mar 26, 28 Introduction, Uninformed Search, Informed Search. AIMA Chapters 1,3
Beam Search
Depth First Branch and Bound
(Extra reading: Ch. 2)
Applications of AI
Intuition of Search Algorithms
Search Algorithms Performance
Pattern Databases
Anytime A*
Additive Pattern Databases
2 Apr 2, 4 Local Search, Constraint Satisfaction, Project 1 AIMA 4.1-4.2, 6
Stochastic Beam Search
Genetic Algorithms
Guide to Constraint Programming
Constraint Programming
3 Apr 9, 11 Constraint Optimization, Logic and Satisfiability Constraint Optimization, AIMA 7, 8.1-8.3
(Extra reading: Ch. 9)

Advanced Constraint Optimization (Chapter 3)
4 Apr 16, 18 Advanced Satisfiability, Probability Basics, Bayesian Networks Advanced SAT Solvers
Phase Transitions
5 Apr 23, 25 Bayes Nets Approximate Inference and Learning, Intro to Machine Learning AIMA 14, 20
Graphical Models
Metropolis-Hastings Monte Carlo

6 Apr 30, May 2 Naive Bayes, Logistic Regression, Text Features, Information Retrieval Naive Bayes vs. Logistic Regression
Text Processing and Information Retrieval
Naive Bayes vs. Logistic Regression

Probabilistic Modeling for Text Analysis

7 May 7, 9 Intro to NLP, Decision Trees, Linear Separators
AIMA 18.1-18.4, 18.6-18.9

8 May 14, 16 Ensembles and Semi-Supervised Learning, Agents, Classical Planning, Project 1 Results
AIMA 18.10, 2, 10 Ensemble Classifiers, Co-training
FF Planner
9 May 21, 23 Adversarial Search, Decision Theory
AIMA 5.1-5.5, 5.6-5.9, 16.1-16.3, 16.6
How Intelligent is Deep Blue?
General Game Playing
10 May 30 Markov Decision Processes, Wrap Up AIMA 17.1-17.3

Monte Carlo Planning
11 June 7 Final Exam, June 7th, 10:30 am, CSE303 Whole Course


Stuart Russell & Peter Norvig, Artificial Intelligence: A Modern Approach,
Prentice-Hall, Third Edition (2009) (required).


Mini-projects: 50%; Written Assignments: 10%; Final: 30%; Class Participation: 10%.

There will be two mini-projects (that fit together into one large system):

The gradebook can be found here.

Course Administration and Policies

Cheating Vs. Collaborating Guidelines

As referenced from Dan Weld's guidelines.

Collaboration is a very good thing. On the other hand, cheating is considered a very serious offense. Please don't do it! Concern about cheating creates an unpleasant environment for everyone. If you cheat, you risk losing your position as a student in the department and the college. The department's policy on cheating is to report any cases to the college cheating committee. What follows afterwards is not fun.

So how do you draw the line between collaboration and cheating? Here's a reasonable set of ground rules. Failure to understand and follow these rules will constitute cheating, and will be dealt with as per University guidelines.

CSE logo Department of Computer Science & Engineering
University of Washington
Box 352350
Seattle, WA  98195-2350
(206) 543-1695 voice, (206) 543-2969 FAX