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,

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. 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).

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 25) and Week 9 (May 30)
Final Othello tournament: 10% of grade (participation-based)
Held Tuesday, June 10, 3:00-5:00PM (Annenberg G21)
Lecture Surveys: 5% of grade
The class will answer surveys about lectures in Blackboard. The answers you give are anonymized, but whether you respond is recorded for each survey. You must answer 2/3 of the surveys to get the full five points. Specifically, you will receive 15*Min(fraction of surveys you answer, 2/3)/2 points for your participation.

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.

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 Sunday, April 13

Problem Set 2 Due 11:59PM Tuesday, April 29

Problem Set 3 Due 11:59PM Thursday, May 8

Problem Set 4 Due 11:59PM Friday, May 23

Problem Set 5 Due 11:59PM Thursday, June 12

Problem Set 6 Due 2:00PM Tuesday, June 10

Lectures (subject to change)

Week 1	Introduction Uninformed Search
Week 2	Informed Search Constraint Satisfaction
Week 3	Local Search (see informed search) Games Chess Games Summary
Week 4	Fun with Search Exam Review Exam
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"

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
Week 2	Ch. 3 (3.5, 3.6) Ch. 6 (through 6.4)
Week 3	Ch. 4 (4.1), Ch. 5
Week 4	None
Week 5	Ch. 7, 8, 9 (can skim 9.3, 9.4)
Week 6	Ch. 13, Ch. 14 (through 14.2)
Week 7	Ch. 18 (through 18.3.2)
Week 8	Ch. 22
Week 9	None
Week 10	Ch. 18.7, Ch. 26, Ch 27

Problem Set 1	Due 11:59PM Sunday, April 13
Problem Set 2	Due 11:59PM Tuesday, April 29
Problem Set 3	Due 11:59PM Thursday, May 8
Problem Set 4	Due 11:59PM Friday, May 23
Problem Set 5	Due 11:59PM Thursday, June 12
Problem Set 6	Due 2:00PM Tuesday, June 10