Intro to Artificial Intelligence (EECS 348)

Spring 2014
Electrical Engineering and Computer Science Department
Northwestern University

Class Meets: 10:00AM-11:00AM MWF Annenberg G21

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: Chandra Sekhar Bhagavatula
Office Hours: 2-3PM Thursday, Ford 3-211
Email: csb <at> u <dot> northwestern <dot> edu,  

Teaching Assistant: Michael Lucas
Office Hours: 4-5PM Thursday, Tech L150
Email: mlucas <at> u <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. The programming assignments will be performed in groups, with separate deliverables for ``programmers'' and ``non-programmers.'' Letter grades are assigned on the standard scale of 93+ is an A, 90-93 is an A-, 87-90 is a B+, etc. We will round to the nearest whole percentage (so 92.55 is an A).

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 23.


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 Sunday, April 13
Problem Set 2Due 11:59PM Tuesday, April 29
Problem Set 3Due 11:59PM Thursday, May 8
Problem Set 4Due 11:59PM Friday, May 23
Problem Set 5Due 11:59PM Thursday, June 12
Problem Set 6Due 2:00PM Tuesday, June 10

Lectures (subject to change)

Week 1Introduction
Uninformed Search
Week 2Informed Search
Constraint Satisfaction
Week 3Local Search (see informed search)
Games Summary
Week 4 Fun with Search
Exam Review
Week 5 Logical Agents
First-order Logic
Inference in First-order Logic
Note typo: bottommost green box on slide #46
should be not Enemy(Nono, America)
Week 6 Logic (continued)
Probabilistic Reasoning
Week 7 Probabilistic Reasoning (cont.)
Intro to Machine Learning
More Machine Learning
Naive Bayes Classifiers
Bayes Nets
Week 8 Decision Trees,
Natural Language Processing
Week 9 Exam Review, Exam
Week 10 Neural networks   See also: NN language modeling intro
Return exam, 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
Week 2Ch. 3 (3.5, 3.6)
Ch. 6 (through 6.4)
Week 3Ch. 4 (4.1), Ch. 5
Week 4None
Week 5Ch. 7, 8, 9 (can skim 9.3, 9.4)
Week 6Ch. 13, Ch. 14 (through 14.2)
Week 7Ch. 18 (through 18.3.2)
Week 8Ch. 22
Week 9None
Week 10Ch. 18.7, Ch. 26, Ch 27