# CSE 335 Topics on Intelligent Decision Support Systems (3)

#### Instructor

Hector Munoz-Avila (Fall 2012)

#### 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, e-commerce, and knowledge management. Credit will not be given for both CSE 335 and CSE 435. Prerequisite: CSE 327 or CSE 109.

#### Textbook

#### References

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

## Course Outcomes

#### Students will have:

- Ability to program conversational case-based reasoners
- Experience encoding knowledge bases for decision support
- Experience programming help-desk systems
- Ability to choose between different knowledge representation formalism based on the target domain
- 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

- Good programming skills
- Understanding of basic notions of modern AI

#### Major Topics Covered in the Course

- Taxonomy of problems
- Introduction to case-based reasoning (CBR)
- Induction of Decision Trees
- Case Representation
- Case Similarity
- Rule-based Systems
- Knowledge-based Design
- Case Retrieval
- Case Adaptation
- Help-desk systems
- E-commerce
- Recommender systems
- Intelligent Tutoring Systems
- Conversational Case-Based Reasoning
- 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 course 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.