CSE 335 Topics on Intelligent Decision Support Systems (3)

Instructor:  Hector Munoz-Avila

Current Catalog Description
Intelligent decision support systems (IDSSs).  AI techniques that are used to build IDSSs: case-based reasoning, decision trees and knowledge representation. Applications of these techniques: help-desk systems, ecommerce, and knowledge management. Credit will not be given for both CSE 335 and CSE 435.  Prerequisite: CSE 327 or CSE 109.

Textbook

"Experience Management:Foundations, Development Methodology, and Internet-Based Applications", by Ralph Bergman

References

"Case-Based Reasoning Technology:From Foundations to Applications", Mario Lenz, Briditte Bartsch-Sporl, Hans-Dieter Burkhard, Stefan Wess

Course Outcomes

Students will have:

1. Ability to program conversational case-based reasoners
2. Experience encoding knowledge bases for decision support
3. Experience programming help-desk systems
4. Ability to choose between different knowledge representation formalism based on the target domain
5. Understanding of wide range of applications for decision support technologies

Relationship between Course Outcomes and Program Outcomes

CSE 335 substantially supports the following program outcomes (in parenthesis I describe the course outcomes that support the program outcomes):

C. An ability to design, implement, and evaluate a computer-based system (supported by course outcomes 1 and 3)

I. An ability to use current techniques, skills, and tools necessary for computing practices (supported by course outcomes 2 and 5)

J. An ability to apply mathematical foundations, algorithmic principles, and computer science theory in the modeling and design of computer-bases systems in a way that demonstrates comprehension of the tradeoffs involved in design choices (supported by course outcome 4)

K. An ability to apply design and development principles in the construction of software systems of varying complexity (supported by course outcomes 1 and 3)

Prerequisites by Topic

1. Good programming skills
2. Understanding of basic notions of modern AI

Major Topics Covered in the Course

1. Taxonomy of problems
2. Introduction to case-based reasoning (CBR)
3. Induction of Decision Trees
4. Case Representation
5. Case Similarity
6. Rule-based Systems
7. Knowledge-based Design
8. Case Retrieval
9. Case Adaptation
10. Help-desk systems
11. E-commerce
12. Recommender systems 13. Intelligent Tutoring Systems
14. Conversational Case-Based Reasoning
15. Case Base Maintenance

Assessment Plan for the Course

The students are given 5 homework assignment. These homework assignments cover outcomes 4 and 5. There is a final exam which test all five cours outcomes. Finally, there are two programming projects. The first covers outcomes 1 and 3 and the second part covers outcome 2.

How the Data in the Course are Used to Assess Program Outcomes: (unless adequately covered already in the assessment discussion under Criterion 4)

Each semester I include the above data from the assessment plan for the course in my self-assessment of the course. This report is reviewed, in turn, by the Curriculum Committee.