CEN556 - Intelligent Systems
Introduction to knowledge based intelligent systems, Rule based expert systems, Fuzzy expert systems, Connectionist neural networks, learning and adaptation, Support Vector machine, Evolutionary algorithms (genetic algorithm, Particle swarm optimization,..). Applications to signals and systems (speech processing, control, image processing and communication).
Course Objectives:
The course aims to introduce students to the basic concept of knowledge representation, problems solving and learning methods of artificial intelligence. At the end of the course students should be able to develop artificial intelligence methods for addressing computer engineering problems such as speech recognition, intelligent control and image recognition.
Prerequisites:
Textbook(s) and/or Other Required Materials:
Primary:
- “Artificial Intelligence: A guide to intelligent systems” by Michael Negnevitsky, Addison-Weslay, 2005.
Supplementary:
- “Neural Networks and learning machine” by Simon Haykin, 3rd Ed, Prentice-Hall, 2009.
- “Artificial Intelligence -A Modern Approach” by S. Russell and Peter Norvig, prentice-Hall.
Course Learning Outcomes: This course requires the student to demonstrate the following:
- Understand knowledge-based intelligent systems, and rule-based expert systems,
- Understand fuzzy expert systems,
- Analyze systems with Artificial Neural Networks,
- Develop classification methods based on Support Vector Machines,
- Use evolutionary computation methods for system optimization,
- Application of artificial intelligence methods for solving computer engineering problems.