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| CSCI 583: Expert Systems and Applications |
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Prerequisites: CSCI 112.
Catalog Description: The basic concepts and techniques of expert systems. Emphasis is on the reasoning behind expert system design, practical problem solving (comparisons and use of expert system shell implementations), and advanced problem solving techniques such as those employed in machine learning and knowledge acquisition. Formerly CSCI 222.
Course Objectives: The primary objective of this course is to provide the student with up-to-date state-of-the-art tools, applications, and techniques for knowledge discovery and knowledge engineering, using intelligent methods.
Specifically:
- Students will study/investigate the history of knowledge-based systems, along with their underlying techniques and methodologies, and the potential future and direction of such systems and their applications. (I)
- Students will learn how to design, implement, and analyze knowledge-based systems, esp. via expert systems using expert system shells. (I)
- Students will develop a fundamental understanding of the applicability of knowledge representation; inferencing; resolution; and retreival techniques, relative to problems/situations requiring non-standard solutions. (I)
- Students will investigate concepts associated with non-traditional databases, such as intelligent databases and scientific databases. (B)
- Students will investigate Knowledge and Data Discovery (KDD) and data mining techniques. (B)
B=Basic I=Intermediate A=Advanced
Course Outcomes:
Students will have developed the ability to:
- Accurately discuss and reflect on historical aspects/systems/limitations of AI at basic level.
- Accurately discuss and reflect on historical aspects/systems/limitations of knowledge-based Systems and Expert Systems.
- Decipher, modify, and implement simple solutions for performing pattern matching, with LISP, AIMA, and CLIPS tools
- Formulate knowledge representations and domain specific rules and constraints.
- Reason about rules of inference for design, implementation and testing of rule-based system.
- Perform research and investigation of knowledge engineering tools, specifically the integration of hybrid intelligent systems for data mining.
- Reason intelligently about the philosophical issues and sociological implications for the future of knowledge engineering, data acquisition, and information usage.
- Perform basic research and critique of Knowledge engineering literature and tools, while honing their own writing and communication skills focusing on scientific writing, precision, and accuracy.
Class/Laboratory Schedule:
Three hours per week in lecture/discussion, minus six hours spent together in lab activity over the course of the semester.
Accreditation Category Content:
This course embodies a significant portion of (a) Theoretical Foundations, (b) Problem Analysis, and (c) Solutions Design. |
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| Topic |
Percentage |
Hours |
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| Algorithms |
20% |
10 |
| Data Structures |
12% |
6 |
| Software Design |
42% |
20 |
| Concepts of Programming |
12% |
6 |
| Computer Organization and Architecture |
2% |
1 |
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Relationship of Course to Program Objectives:
This course supports the achievement of the following program objectives:
- All students will be able to analyze and solve computing problems, or problems in related areas, and to continually upgrade their knowledge and skills. (P)
- All students will be effective oral and written communicators and be able to function effectively as members of multi-disciplinary teams. (P)
- All students will have an appreciation for the individual, society, and human heritage and they will be aware of the impact of their work on society and the environment. (R)
- Those graduates who pursue careers as computing professionals will have the skills to use and design new and innovative systems that meet society's needs. (P)
- Those graduates who pursue advanced degrees will have the skills to succeed in graduate programs in computing and related fields. (R)
I=Introduced P=Practiced R=Reinforced |
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