Electrical & Computer Engin 597C - ST-Intelligent Systems

Introduction to the tools and methods in the design, analysis, optimization, and control of industrial systems. Topics include neural networks and their application in complex system modeling, fuzzy logic, information fusion methods, and optimization schemes. MATLAB used as the software platform. Prerequisites: differential equations, linear algebra, basic probability.

Sr's or Grad in Engineering We are quickly entering the era of intelligent systems. Many such systems are based on pattern recognition. In this course we introduce concepts and mathematical methods for pattern recognition. The topics we will cover are:

1. Classifiers based on Bayes decision theory;
2. Classifiers based on cost function optimization;
3. Data transformation: feature generation and dimension reduction;
4. Feature selection;
5. Template matching;
6. Hidden Markov models;
7. Clustering.

These are in line with the book. The prerequisites are basic linear algebra and basic probability theory. The course requirement include 8 homework sets (10%); 2 exams (50%), and one final term paper (40%).

Instructor is Weibo Gong, KEB211E, (413)545-0384, and I will give you my skype name so you can contact me 7X24 for questions.