EECS 391/491: Introduction to Artificial Intelligence (Spring 2009)

Instructor: Soumya Ray
- Office: Olin 516
- Office hours: 3-4:30pm Monday, or by appointment

Meeting Time and Location: MW 9-10:15, Bingham 103

Textbook: AI: A Modern Approach, 2ed, by Stuart Russell and Peter Norvig

Books at KSL (2 hour reserve):
- AI: A modern approach, Russell & Norvig
- AI: A new synthesis, Nilsson

TAs: Salim Akhter Chowdhury (salimakhter.chowdhury_AT_case) and Gang Shu (gang.shu_AT_case)

Office: Olin 504
Office hours: Salim: 12-1pm Friday, Gang: 1-2pm Wednesday

News

1/11 Course website is up.
1/26 Written homework 1 is online, due Feb 4.
1/26 Programming assignment 1 is online, due Feb 18.
2/2 Two makeup lectures are scheduled on Thursday 4:10-5pm and Friday 4-4:50pm in Bingham 103. Please try to attend any ONE.
2/4 Written homework 2 is online, due Feb 16.
2/11 HW1 grades posted on blackboard; please collect answer scripts from TAs during their office hours.
2/18 Written homework 3 is online, due March 2.
2/23 Programming Assignment 2 is online, due March 23.
2/23 EECS 491 students: Think about projects. I will meet with you next week to finalize your projects.
2/23 If HW3 is turned in after the solution is posted on 3/3, it will incur a 50% penalty.
2/23 Midterm exam on March 4th in class. One sheet of notes and a calculator allowed. Material up to 2/25 will be tested.
3/11 Midterm grades posted.
3/11 Final Exam schedule: May 5, 9am-11am, Bingham 103.
3/16 Written homework 4 is online, due March 30.
3/25 Programming Assignment 3 is online, due April 15.
3/26 Gang Shu's office hours have changed to 1-2pm Wednesday.
3/30 Written homework 5 is online, due April 13.
3/31 EECS 491 students: Document containing instructions about project report is available below.
4/6 EECS 491 students: Project deadline postponed to April 27.
4/13 Written homework 6 is online, due April 22.
4/13 Programming Assignment 3 deadline postponed to April 17 (Friday).
4/28 Thanks for attending everyone! Please remember to fill out the course evaluations.
5/6 Final Grades assigned.

Overview

This course is an introduction to artificial intelligence. We will study the concepts that underlie intelligent
systems. Topics covered include problem solving with search, constraint satisfaction, adversarial games,
knowledge representation and reasoning using propositional and first order logics, reasoning under
uncertainty, introduction to machine learning, introduction to classical planning and reinforcement learning,
introduction to computer vision and natural language processing.

Grading

Homework: 30% (EECS 491: 20%)
Programming Assignments: 30% (EECS 491: 20%)
Midterm Exam: 15%
Final Exam: 20%
Class Participation: 5%

EECS 491 students will do a class project, which will count for 20% of the grade.
EECS 391 and 491 will be graded on separate curves.
Assignments are due by end of class on the due date. Late assignments will be penalized by 10% for each extra day after the due date.
Case Academic Integrity Policy

Lecture Notes

Course Mechanics Syllabus

Date	Topic	Textbook Chapter	Lecture Notes
Jan 14	Introduction	Chapter 1	Lecture 1
Jan 21	Intelligent Agents	Chapter 2	Lecture 2
Jan 26	Uninformed Search	Chapter 3 (skim 3.6)	Lecture 3
Jan 28	Informed Search	Chapter 4	Lecture 4
Feb 2	Informed Search	Chapter 4	See Jan 28 slides
Feb 4	Search for Optimization	Chapter 4 (skim 4.5)	Lecture 5
Feb 5/6 (makeup)	Constraint Satisfaction Problems	Chapter 5	See Feb 9 slides
Feb 9	Constraint Satisfaction Problems	Chapter 5 (skim 5.4)	Lecture 6
Feb 11	Adversarial Search	Chapter 6 (skim 6.6)	Lecture 7
Feb 16	Review of Search	Chapters 3-6	Lecture 8
Feb 18	Basic Probability	Chapter 13	Lecture 9
Feb 23	Machine Learning; Naive Bayes	Chapter 18.1-2,20.1-2	Lecture 10, Reading, Table 3.2
Feb 25	Evaluating Learning Algorithms	Chapter 18.3	Lecture 11
March 2	Decision Tree Induction	Chapter 18.3	Lecture 12, Reading
March 4	Midterm Exam
March 9,11	Spring Break
March 16	Overfitting, Comparing Learning Algorithms, Midterm review	Chapter 18.3	Lecture 13
March 18	Reinforcement Learning	Chapter 17.1-17.3	Lecture 14
March 23	Reinforcement Learning	Chapter 21 (skim 21.5)	Lecture 15
March 25	Reinforcement Learning	Chapter 21 (skim 21.5)	See Lecture 15 slides
March 30	Machine Learning Review		Lecture 16
April 1	Propositional Logic	Chapter 7	Lecture 17
April 6	Inference in Propositional Logic	Chapter 7	Lecture 18
April 8	First Order Logic	Chapter 8	Lecture 19
April 13	Inference in FOL	Chapter 9	Lecture 20
April 15	Inference in FOL	Chapter 9	Lecture 21
April 20	Planning	Chapter 11 (skim 11.4-5)	Lecture 22
April 22	Planning	Chapter 11 (skim 11.4-5)	Lecture 23
April 27	Wrapup		Lecture 24
May 5	Final Exam

Assignments

All dates below are tentative until the assignments are actually posted.

Assignment	Date Assigned	Date Due	Solutions/Data/Code
Written 1	Jan 26	Feb 4	Solution 1
Written 2	Feb 4	Feb 16	Solution 2
Written 3	Feb 18	Mar 2	Solution 3
Written 4	Mar 16	Mar 30	Solution 4
Written 5	Apr 1	Apr 13	Solution 5
Written 6	Apr 15	Apr 22	Solution 6
Programming 1	Jan 26	Feb 18
Programming 2	Feb 23	Mar 23	Example, p1, p2, p3
Programming 3	Mar 25	Apr 17 (Extended)	Code
EECS 491 projects	Mar 9	Apr 27 (Extended)	Report Instructions

Fun Stuff

Last update 5/7/2009 12:51PM by Soumya Ray