EECS 391: Introduction to Artificial Intelligence (Spring 2012)

• Instructor: Soumya Ray
• Office: Olin 516
• Office hours: 2:30pm-4:00pm Tuesday, or by appointment

 

• Meeting Time and Location: TR 10-11:15, Bingham 103

 

• Textbook: AI: A Modern Approach, 3ed, by Stuart Russell and Peter Norvig

 

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

 

• TA:Feng Cao
• Office: Olin 806
• Office hours: Thursday 2:30-4 or by appointment

News

1/16 Course website is up.
1/19 HW1 is posted, due 1/26.
1/19 PA1 is posted, due 2/7.
1/31 HW2 is posted, due 2/7.
2/4 HW2 due date postponed to Feb 9, Quiz 2 on Feb 14.
2/9 PA2 is posted, due 2/28.
2/14 HW3 is posted, due 2/21.

3/19 HW5 is posted, due 3/27.

3/22 PA4 is posted, due 4/10.

3/27 HW6 is posted, due 4/5.

4/14 HW7 is posted, due 4/24.

4/17 PA5 and PA6 posted, due 5/3.

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, and introduction to classical planning and reinforcement learning.

Grading Policies

Homework: 30%
Programming Assignments: 40%
Quizzes (6 best): 30%
Quality Class Participation: 2%

• Written assignments are due by 5pm on the due date. Late assignments will be penalized by 10% for each extra day after the due date.
• Solutions to written assignments will be posted at noon on the day before the respective Quiz. If you submit your homework after the solution is online, you will lose an additional 50% of your score.
• Programming assignments are due by midnight on the due date in Blackboard. Late assignments will be penalized by 10% for each extra day after the due date.
• Collaboration policy:
• You are welcome to discuss assignments with each other, with the TA and with me, but do not copy solutions from any source.
  All submitted work must be solely the work of the people submitting them.
• You can do all written and programming assignments in pairs. If you pair up for an assignment then both individuals receive the same score.
• Case Academic Integrity Policy

 

Lecture Notes

Course Mechanics

Date	Topic	Textbook Chapter	Lecture Notes	Videos
	Part 1: Problem Solving With Search
Jan 17	Preliminaries
Jan 19	Introduction and Intelligent Agents	Chapter 1&2	Lecture 1
Jan 24	Uninformed Search	Chapter 3	Lecture 2
Jan 26	Informed Search	Chapter 3	Lecture 3
Jan 31	Search for Optimization (Quiz 1)	Chapter 4.1-2	Lecture 4
Feb 2	Search for Optimization, Adversarial Search	Chapter 5.1-5	Lecture 4(2)
Feb 7	Adversarial Search, Review of Search	Chapter 5.1-5	Lecture 5
	Part 2: Logic and Automated Planning
Feb 9	Propositional Logic	Chapter 7	Lecture 6
Feb 14	(Quiz 2)	Chapters 1-5	Lecture 7(1)
Feb 16	Inference in Propositional Logic, First Order Logic	Chapter 7	Lecture 7(2)
Feb 21	First Order Logic	Chapter 8.1-3	Lecture 8
Feb 23	Inference in FOL (Quiz 3)	Chapter 9
Feb 28	Inference in FOL and Automated Planning	Chapter 9, 10.1-2, 10.4.2, 10.4.4	Lecture 8(2)
March 1	Automated Planning	Chapter 10.1-2, 10.4.2, 10.4.4	Lecture 8(3)
	Part 3: Probability and Machine Learning
March 6	Basic Probability	Chapter 13	Lecture 9
March 8	(Quiz 4)	Chapter 7-10
March 13,15	Spring Break
March 20	Probabilistic Inference, Bayesian Networks	Chapter 13, 14.1,2,4.1,4.2, 5.1	Lecture 10
March 22	Bayesian Networks, DBNs	Chapter 14.1,2,4.1,4.2,5.1, 15.5	Lecture 11	Localization with Particle Filters video
March 27	Machine Learning, Probabilistic Classification	Chapter 18.1-2, 20.1-2	Lecture 12
March 29	Probabilistic Classification (Quiz 5)	Notes	Notes
April 3	Maximum Likelihood Estimation, Perceptrons	Chapter 18.7	Lecture 12(2) , Lecture 13(1)
April 5	Artificial Neural Networks, Evaluating Learning Algorithms	Chapter 18.4	Lecture 13(2)
April 10	Evaluating Learning Algorithms (Quiz 6)	Chapter 13,14,18
	Part 4: Decision Making under Uncertainty and NLP
April 12	Sequential Decision Making	Chapter 17.1-3	Lecture 14(1)
April 17	Reinforcement Learning	Chapter 21	Lecture 14(2)	Helicopter training, Helicopter video
April 19	Reinforcement Learning	Chapter 21	Lecture 14(3)	Q Learning Video
April 24	Natural Language Processing	Chapter 22
April 26	(Quiz 7)	Everything

Assignments and Quizzes

The assignment dates below are tentative until the assignments are actually posted.

Assignment	Date Assigned	Date Due	Solutions/Data/Code/Topics for Quizzes
Written 1	Jan 19	Jan 26	Solution
Quiz 1	Jan 31		Intelligent Agents, Uninformed and Informed Search (Ch 2-3)
Written 2	Jan 31	Feb 7 (Feb 9)	Solution
Quiz 2	Feb 14		Search
Written 3	Feb 14	Feb 21	Solution
Quiz 3	Feb 23		Propositional and First Order Logic
Written 4	Feb 28	Mar 6	Solution
Quiz 4	Mar 8		Logic and Planning
Written 5	Mar 20	March 27	Solution
Quiz 5	March 29		Probability
Written 6	Mar 27	Apr 5 (Apr 6)	Solution
Quiz 6	Apr 10		Probability and Machine Learning
Written 7	Apr 14	Apr 24	Solution
Quiz 7	Apr 26		Everything
Programming 1	Jan 19	Feb 7	SimpleRTS, JAR file for JRE>=1.7, Maze maps
Programming 2	Feb 9	Feb 28	Data
Programming 3	Mar 1	Mar 20
Programming 4	Mar 22	Apr 10	Data
Programming 5	Apr 17	May 3	Data
Programming 6	Apr 17	May 3 (May 6)	Data

Fun Stuff

• IBM's Watson System
• Marion Tinsley: Human Perfection at Checkers?
• Ravens and Intelligence
• The Chess Master and the Computer by Garry Kasparov
• Your next car will drive itself---Wired.com article
• Say Hello To Stanley---Wired.com article on the car that won the DARPA Grand Challenge
• Winning the Grand Challenge by Sebastian Thrun (Team Leader for Stanley)
• Case Urban Challenge
• Loebner Prize for AI
• Netflix Prize
• Association for the Advancement of Artificial Intelligence (AAAI)
• When will computer hardware match the human brain? by Hans Moravec
• Ray Kurzweil's site
• Alan Turing's article describing the Turing test
• Gerry Tesauro's article on TD-Gammon
• reddit.com Interview with Peter Norvig