Download EECE 503 – SPECIAL TOPICS: Artificial Intelligence and its

EECE 503 – SPECIAL TOPICS: Artificial Intelligence and its Applications
(3 credits)
Catalog description
The course aims at giving students an insight into artificial intelligence as an evolving field. It covers
background material, applications of artificial intelligence, central problems of artificial intelligence,
knowledge representation, control and inference. The course then focuses on search algorithms,
machine learning, and natural language processing.
Required or elective
Elective for CCE / EE and other engineering students who have taken at least a first course in
programming and data structures.
Prerequisite
EECE330 (Data Structures and Algorithms)
Topics
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Introduction
Knowledge Representation in Logic
Inference in Predicate Calculus
Prolog- Programming in Logic
Frames, Semantic Nets, Hierarchal Nets, Part-of Nets, Discrimination Nets
Production/Expert Systems
Search Algorithms: Depth-1st, Breadth-1st, Best-1st, A*-Search, the British Museum, Genetic
Algorithms, Guided Search
Machine Learning
Natural Language Processing
Textbook(s) and/or required materials
Artificial Intelligence, Fifth Edition, by George Luger, A.W. 2005
Course objectives
The objectives of this course are to give students:
1. An understanding of the fundamentals of knowledge representation
for use in machine intelligence.
2. An overview of most recent trends in search algorithms applicable to
solving many real world problems, such as scheduling.
3. An overview of some inference systems, with focus on prolog and
logic.
4. The basic analysis of natural languages and their understanding.
5. A hands-on experience in designing knowledge-based systems
Topics covered
1. AI background/historical overview
2. Overview of AI central problems
3. Issues in KR
4. Predicate Logic in KR and Inference
5. Frames
6. Semantic Nets, Part-of and Function-of hierarchies
7. Production/Expert Systems
8. Programming in Prolog
75 min lectures
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9. Search Algorithms in AI
10. Natural Language Processing
11. Machine learning
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Class/laboratory schedule
a- Two 75-minute lectures per week.
b- Use of computer lab needed for running Prolog.
Course outcomes
At the end of the course, students:
1. Are well rounded with the concepts of AI and its applications.
2. Are familiar with Prolog.
3. Understand the concept of Expert Systems
4. Understand the concept of Knowledge Representation and related
techniques
5. Understand the concept and meaning of robotics
6. Can apply search algorithms to solving AI and engineering problems
7. Are able to develop preliminary Natural Language translators
8. Understand the concept of Intelligent Resource Management
9. Understand the concept/applications of Machine Learning
10. Are familiar with current research trends in AI and relationship to
Engineering
Resources of the course
Reference books, reading materials / Industrial applications notes, articles in journals and conference
proceedings, and WebCT.
Evaluation methods
Midterm (30%), final exam (35%), four assignments (10%), and two projects (25%)
Professional component
Engineering topics: 100%
General education: 0%
Mathematics and basic sciences: 0%
Computer usage
Prolog and C++.
Person(s) who prepared this description and date of preparation
Issam A.R. Moghrabi, March 2006.