Course Expectations and Tentative Syllabus
CIS:655 Intelligent Systems Technology Summer 2005
Olney Hall, Room 201 Wed 6:00-9:15pm
Professor: Dr. Michael Redmond
330 Olney Hall (215) 951-1096
http://www.lasalle.edu/~redmond/teach/655
Office Hours: Wed 5:00-6:00pm
And at other times by appointment. Also, by phone and e-mail.
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
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 25%
Final Exam 40%
Assignments 30%
Class Participation 5%
Grade Scale:
A 92-100
A- 90-91
B+ 88-89
B 82-87
B- 80-81
C 60-79
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.
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.
Tentative Course Plan:
Date Material Reading
May 18 Intro to Class,
Intro to Knowledge-Based Systems Chapt 1
May 25 Rule-Based Expert Systems Chapt 2
June 1 Uncertainty Management Chapt 3
June 8 Fuzzy Logic and Expert Systems, Chapt 4
June 15 Case-Based Reasoning supplemental
June 22 MIDTERM
June 29 Artificial Neural Networks Chapt 6
July 6 Evolutionary Computation Chapt 7
July 13 Knowledge Engineering and Data Mining Chapt 9
July 20 Machine Learning and more Data Mining supplemental
July 27 Natural Language Understanding supplemental
Aug 3 Final Exam