Intro to Artificial Intelligence (EECS 348)

Spring 2013
Electrical Engineering and Computer Science Department
Northwestern University

Class Meets: 11:00AM-11:50PM MWF Tech LR4

Instructor: Doug Downey
Office Hours: Tuesdays, 3:00-4:00 (or by appt), Ford 3-345
Email: ddowney <at> eecs <dot> northwestern <dot> edu

Teaching Assistant: Besim Avci
Office Hours: 1:00-2:00PM Thursday L580
Email: besim <dot> namik <dot> avci <at> gmail <dot> com

Teaching Trainee:Yu Cheng
Office Hours: 4:00-5:00PM Friday, L460
Email: ych133 <at> eecs <dot> northwestern <dot> edu



Artificial Intelligence: A Modern Approach, Stuart Russell and Peter Norvig, Prentice Hall (2009). The latest version is the third edition, though you can get through the course using the second edition.


The majority of the course grade is based on problem sets assigned roughly weekly. The problem sets will involve both exercises and programming.

Mid-term 1

Mid-term 1 will cover search (uninformed and heuristic), constraint satisfaction, and games. A Practice Exam is available and will be discussed in class on April 24.


Programming assignments will require that you program in your choice of C++ or Python; we will provide starter code that implements basic functionality for each assignment. If you're not familiar with either language, here is a Python tutorial to get you started.

Problem Set 1Due 11:59PM Thursday, April 11
Problem Set 2Due 11:59PM Tuesday, April 23
Problem Set 3Due 11:59PM Tuesday, May 7
Problem Set 4Due 11:59PM Friday, May 17
Problem Set 5Due 11:59PM Thursday, May 30
Problem Set 6Due 11:59PM Sunday, June 9


Week 1Introduction
Week 2Informed Search
Constraint Satisfaction
Week 3Constraint Satisfaction (cont.)
Games Summary
Week 4 Fun with Search
Week 5 Agents
Logical Agents
Week 6 First-order Logic
Inference in First-order Logic
Note typo: bottommost green box on slide #46
should be not Enemy(Nono, America)
Week 7 Probabilistic Reasoning
Intro to Machine Learning
Week 8 (briefly) More Machine Learning
Naive Bayes Classifiers
Bayes Nets
Friday: Brenna Argall Guest Lecture
Week 9 Decision Trees
Week 10 Exam Review, Neural Networks, the "big questions"


Given in terms of the 3rd edition; if you're using the 2nd edition, check the 3rd edition's table of contents for correspondences.
Week 1Ch. 1 & 3 (through 3.4)
Week 2Ch. 3 (3.5, 3.6)
Ch. 4 (4.1)
Ch. 6 (through 6.4)
Week 3Ch. 5
Week 4None
Week 5Ch. 2, Ch. 7
Week 6Ch. 8, Ch. 9 (can skim 9.3, 9.4)
Week 7Ch. 18, 13
Week 8Ch. 14
Week 9Ch. 20 (only through 20.2)
Week 10Ch. 26, Ch 27