Course Expectations and Tentative Syllabus

 

CSC:456                               Artificial Intelligence                                                                           Spring 2008

                                                Olney Hall,  Room 201                                                                      MWF  10:00-10:50am

 

Professor:                            Dr. Michael Redmond   

                                                330 Olney Hall  (215) 951-1096

                                                redmond@lasalle.edu

                                                http://www.lasalle.edu/~redmond/teach/456

 

Office Hours:                      MWF 9:00-9:50am

                                                MWF  11:00-11:50am             

And at other times by appointment

 

Text:

                Negnevitsky, M., The Artificial Intelligence, A Guide to Intelligent Systems, Second Edition, Addison Wesley, 2005, ISBN: 0-321-20466-2

 

Course Description:

                This course will examine intelligent systems technologies that have or may become practical for organizational use. Topics include simple expert systems and expert systems with certainty factors, case-based reasoning, machine learning, neural networks, genetic algorithms, fuzzy logic, and (time permitting) natural language understanding. Intelligent systems technologies will be analyzed and evaluated with consideration for practical use. The textbook is geared toward the underlying ideas, we will try to use as many of the techniques as possible. In addition, just for fun, we will look at methods for two person strategy games.

The catalog prerequisite is not accurate; there is no dependence on database concepts. The course relies on the ability to understand algorithms, some of which are mathematical.

 

Grading:

 

   Midterm                                                             20%

   Final Exam                                                        30%

   Assignments                                                      20%

   Project                                                                                25%   (5%, 5%, 15%)

   Class Participation                                             5%

                                                               

   Grade Scale:

 

 

 

B+

88-89

C+

78-79

D+

68-69

 

A

92-100

B

82-87

C

72-77

D

60-67

 

A-

90-91

B-

80-81

C-

70-71

 

 

 

 

 

 

 

 

 

F

< 60

No make up exams unless arranged in advance.

Final exam is cumulative, but will focus more heavily on the (previously untested) final half of the course.

There will be several, varied assignments over the course of the semester. One will involve using Weka data mining software. This software is freely downloadable over the WWW so should be able to be used outside La Salle. The others are still to be determined. The assignment due dates will be specified when they are assigned.

Projects will involve a game playing program for a game to be announced – see future handout. While game playing isn’t a particularly important area of AI, it should be a fun project. Projects will be due in 3 stages. Note that it is due before the last week of classes!!! After the final version is turned in, I will run a tournament among the working programs. The winning program gets extra credit.

 

 


                Course Objectives

 

Concepts:

 

1.       The student should understand how the covered algorithms work (to a level of detail that given sufficient time they could reproduce the program):

·         Expert system forward chaining

·         Expert system backward chaining

·         Expert system combining certainty factors

·         Fuzzy expert system

·         Case-based reasoning

·         Neural networks

·         Genetic algorithms

·         major Data Mining algorithms

 

2.       The student should understand the strengths and weaknesses of the covered algorithms.

 

3.       (time permitting) The student should understand basic concepts related to natural language understanding, and when it might be useful to an organization. 

 

Applications:

 

1.       The student should gain some exposure and experience with commercial/ or public domain tools involving intelligent systems technology(s).

2.       The student should understand what kinds of tasks different covered algorithms might be appropriate for.

3.       The student should be able to complete a small AI project.

4.       The student should have fun.

 

 

Tentative Course Plan:

 

Date                      

Material                         

Reading

Project Due

1/14

Intro to Class

 

 

1/16, 1/18, 1/23, 1/25

Intro to Knowledge-Based Systems

Chapt 1

 

1/28, 1/30, 2/1, 2/4

Game Playing Search

Supplemental

 

2/6, 2/8, 2/11

Rule-Based Expert Systems

Chapt 2

Project Stage 1 (2/11)

2/13, 2/15, 2/18

Uncertainty Management

Chapt 3

 

2/20, 2/22, 2/25

Fuzzy Logic and Expert Systems

Chapt 4

 

2/29

MIDTERM EXAM

 

 

2/27, 3/10, 3/12

Case-Based Reasoning

Supplemental

Project Stage 2 (3/12)

3/14, 3/17, 3/19, 3/26

Artificial Neural Networks

Chapt 6

 

3/28, 3/31, 4/2, 4/4

Evolutionary Computation

Chapt 7

 

4/7, 4/9, 4/11

Knowledge Engineering and Data Mining

Chapt 9

 

4/14, 4/16

Machine Learning and more Data Mining

Supplemental

 

4/18, 4/21, 4/23

Natural Language Understanding

Supplemental

Project Stage 3 (4/18)

4/25

Catch up

 

 

TBD – between 4/28 and 5/2

Final Exam

 

 

 

 MLK Jr HOLIDAY – Jan 21

SPRING BREAK – March 3-7

EASTER  BREAK – March 21-24