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
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