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

Policies

Textbook

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.

Grading

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

Problem Sets (6): 55% of grade
Each set is worth approximately 10 points
Late assignments penalized 10% per day
Midterms (2): 30% of grade
To be held on Fridays of Week 4 (April 26) and Week 9 (May 31)
Final Othello tournament: 10% of grade (participation-based)
Held Monday, June 10, 3:00-5:00PM (Tech LR4)
Collective Lecture Ratings: 5% of grade
The class will rate lectures using anonymous surveys in Blackboard. Every student gets the same grade for this component, which is calculated as the fraction of lectures for which at least 1/3 of the class responds to the survey. You have until noon of the day following the lecture to provide your rating.

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.

Homeworks

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 1 Due 11:59PM Thursday, April 11

Problem Set 2 Due 11:59PM Tuesday, April 23

Problem Set 3 Due 11:59PM Tuesday, May 7

Problem Set 4 Due 11:59PM Friday, May 17

Problem Set 5 Due 11:59PM Thursday, May 30

Problem Set 6 Due 11:59PM Sunday, June 9

Lectures

Week 1	Introduction Search
Week 2	Informed Search Constraint Satisfaction
Week 3	Constraint Satisfaction (cont.) Games Chess 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 Exam
Week 10	Exam Review, Neural Networks, the "big questions"

Reading

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 1	Ch. 1 & 3 (through 3.4)
Week 2	Ch. 3 (3.5, 3.6) Ch. 4 (4.1) Ch. 6 (through 6.4)
Week 3	Ch. 5
Week 4	None
Week 5	Ch. 2, Ch. 7
Week 6	Ch. 8, Ch. 9 (can skim 9.3, 9.4)
Week 7	Ch. 18, 13
Week 8	Ch. 14
Week 9	Ch. 20 (only through 20.2)
Week 10	Ch. 26, Ch 27

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