CSCI 487: Artificial Intelligence

Fall, 2005

 

Department of Computer Information Science

Indiana University—Purdue University, Indianapolis

 

 

Instructor: Dr. Yuanshun Dai

Course: Room No: SL210, M/W 5:45pm-7:00pm.

 

Instructor Office Hours: Wednesday: 3:00pm – 4:00 pm.

Room: SL280L

Phone: 317-274-3473

E-mail Address: YDai@cs.iupui.edu

 

TA- TBA, Office Hours: TBA.

 

Book: Stuart Russell and Peter Norvig, "Artificial Intelligence: A Modern Approach." 2nd Edition, Prentice Hall, 2003.

ISBN: 0-13-790395-2

 

 

DESCRIPTION:

 

Study of key concepts and applications of artificial intelligence. Problem-solving methods, state space search, heuristic search, knowledge representation: predicate logic, resolution, natural deduction, nonmonotonic reasoning, semantic networks, conceptual dependency, frames, scripts, and statistical reasoning; advanced AI topics in game playing, planning, learning, and connectionist models.
　

 

PREREQUISITES:   CSCI 362.

 

 

GENERAL POLICY

 

You should make every effort to attend all lectures.  Missed lecture notes should be obtained from fellow students.  Handouts can be obtained from me personally or via electronic means.  Exams will be announced approximately one week in advance.  It is the responsibility of the student to notify the instructor in advance if the student cannot attend a regularly scheduled exam.

 

COURTESY

 

It is expected that students will conduct themselves in a courteous manner to the professor and fellow students.  That includes no cell-phone calls, minimal talking in class, and no other actions that are disruptive to the class.  Make every effort to arrive on time to class.

 

HOMEWORK POLICY

 

Assignments will normally be due one week after they are assigned.  Assignments can be submitted up to one week late for half credit.  Assignments submitted later than one week past the due date will receive no credit.

 

 

ATTENDANCE POLICY

 

Students are expected to attend ALL lecture sessions.  Failure to attend may affect you grade.  Students are responsible for material covered on the days they miss.  Students are encouraged to actively participate in the class in a constructive manner.

 

GRADING

 

       Exam 1 -------                30%

       Homework ---------       30%

       Exam 2 --------              30%

       Attendance -------          10%

                                           -------

                                            100%

Grading scale: 

<50

>=50

>=55

>=60

>=65

>=70

>=73

>=77

>=80

>=83

>=87

>=90

>=95

F

D-

D

D+

C-

C

C+

B-

B

B+

A-

A

A+

 

 

SCHEDULE (The notes are subjected to change)

 

Class

Date

 Day

Lecture

Notes

Chpt(s)

Due

1

8/24

  W

Introduction

Intro

1

 

2

8/29

 M

Intelligent Agent I

  Zip1, Zip2

2

 

3

8/31

 W

Intelligent Agent II, and Problem-Solving

 　

2, 3

 

4

9/7

 W
Problem-solving I
 　

3

 

5

9/12

 M

Problem-Solving II

 　

3

HW1

6

9/14

 W

Basic Searching, Improved Searching

 　

3, 4

 

7

9/19

 M

Improved Searching

 　

4

 

8

9/21

 W

Heuristic Algorithm I

 　

4

 

9

9/26

 M

Heuristic Algorithm II

 　

4

HW2

10

9/28

 W

Logical Agents I

 　

7

 

11

10/3

 M

Logical Agents II

 　

7

 

12

10/5

 W

Logical Agents III

 　

7

HW3

13

10/10

 M

First-Order Logic I

 　

8

 

14

10/12

 W

First-Order Logic II, and Inference I

 　

8, 9

 

15

10/17

 M

Review

 　

 

　

16

10/19

 W

Exam 1

　

　

 

17

10/24

 M

Inference II

 　

9

 

18

10/26

 W

Uncertainty I

 　

13

 

19

10/31

 M

Bayesian Network

 　

14

 

20

11/2

 W

Modeling I

 　

15

 

21

11/7

 M

Modeling II

 　

15

HW4

22

11/9

 W

Speech Recognition

　

15

 

23

11/14

 M

Rational Decision

 　

16

 

24

11/16

 W Learning I 　         20

　

25

11/21

 M Neural Network, 　         20

 

26

11/28

 M

Robert

 　

25

 

27

11/30

 W

(Take home Exam 2)

 1 Week for you to finish

　

 
　 　 　 　 　 　

HW5

Final

Dec 7

 　

Submit the Exam 2 (by 5:00pm)