CSE440: Introduction to Artificial Intelligence

Fall 2012

Time

10:20-11:40am, Monday and Wednesday

Location:

1230 Engineering Building 

Professor:

Joyce Chai, 2138 Engineering Building, 517-432-9239, jchai AT cse DOT msu DOT edu

Office Hours:

Monday, Wednesday:  11:40-12:40pm, or by appointment

Textbook:

Artificial Intelligence: A Modern Approach (3rd Edition)  by Stuart Russell and Peter Norvig, Prentice Hall, 2010

Teaching Assistant:

Masoud Mirmomeni, mirmomeny@gmail.com, mirmomen@msu.edu 

TA Office Hours:

Wednesday:  3:00-4:30pm and Thursday 3:00-4:30pm , EB3203

Course Description:

This is an introduction course to artificial intelligence covering fundamental topics in problem solving, heuristic search, knowledge representation, inference, planning, probabilistic reasoning, learning, and natural-language processing. 

Course Grades:

Six homework Assignments

30%

Three programming projects

18%

Attendance

2%

Midterm 1

15%

Midterm 2

15%

Final Exam

20%

Homework and Examinations:

The work in this course consists of six written homework assignments, three programming projects, two midterm exams, and one final exam. The written assignments must be turned in at the beginning of lecture on the day it is due. The programming projects are due before the midnight of the due date (through handin facility). No late homework will be accepted. Exams will be close book. There will be NO make-up exams except under extremely exceptional circumstances which must be documented and discussed with the professor ahead of time. The final exam will be comprehensive. 

Due date

Homework 1:

September 17

Homework 2:

October 3

Homework 3:

October 22

Homework 4:

November 5

Homework 5:

November 26

Homework 6:

December 5

Programming project 1

September 26

Programming project 2

October 29

Programming project 3

December 7

Midterm 1

October 8 (Monday)

Midterm 2

November 12 (Monday)

Final Exam:

Monday, December 10, 12:45-2:45 p.m.

Tentative Schedule of Topics

Topic

Reading

Aug. 29

Introduction 

Chapters 1

Sept. 5

Intelligent Agent, Search

Chapter 2

Sept. 10, 12

More search

Chapter 3

Sept. 17, 19

Games, Constraint Satisfaction

Chapter 5.1-3, Chapter 6.1-5

Sept. 24, 26

Propositional Logic and FOL

Chapter 7, Chapter 8.1-4

Oct. 1, 3

Logic and Inference

Chapter 9.1-5

Oct. 8, 10

Midterm 1 and Prolog

Programming in Prolog, Chapter 1-4

Oct. 15, 17

DCG and Parsing in Prolog

Programming in Prolog, Chapter 9

Oct. 22, 24

Planning, Knowledge representation,

Chapter 10.1.1-10.2.2, Chapter 12. 1-3,

Oct. 29, 31

Uncertainties, Bayes rules, Review

Chapter 13,

Nov. 5,  7

Bayesian Reasoning, Naïve Bayesian classifier 

Chapter 14.1-3

Nov. 12, 14

Midterm 2, Bayesian Network 

Chapter 14.4-5, 

Nov. 19, 21

Decision Trees

Chapter 18. 1-3

Nov. 26, 28

Concept learning, NLP

Chapter 18.7.1-18.7.3, Chapter 19. 1,

Dec. 3, 5

Robotics, Review

Chapter 17.5.1,

Class notes are here  for your reference. 

Academic Honesty:

Your grade should reflect your own work. Copying or paraphrasing someone's work (code included), or permitting your own work to be copied or paraphrased, even if only in part, is not allowed, and will result in an automatic grade of 0 for the entire assignment in which the copying or paraphrasing was done. Please talk to the instructor if you have trouble completing an assignment.

Alternative Testing:

Alternative testing is available to those with a documented disability affecting performance on tests. Students with documented disabilities requiring some form of accommodation receive a Verified Individualized Services and Accommodations (VISA) document which displays verified testing accommodations when appropriate. Please visit Alternative Testing Guidelines if applied. 

Notes: The instructor reserves the right to modify course policies and the course calendar according to the progress and needs of the class.