CSCI 4830 Non-Symbolic Artificial Intelligence
Prerequisites: Freshman math such as calculus, high-level language programming (e.g., C, C++ or Java), and data structures. Knowledge of an AI language is not required.
Instructor: Toshinori Munakata, Professor
and Information Science Department,
Textbook: Toshinori Munakata, Fundamentals of the New Artificial Intelligence: Neural, Evolutionary, Fuzzy and More, 2nd Ed., Texts in Computer Science, Springer, 2008*.
This course introduces newer AI fields such as neural networks, genetic algorithms (or evolutionary computing), and fuzzy logic. These techniques have extensively been applied in industrial, governmental and commercial domains. Take this rare opportunity to learn state-of-the-art AI techniques directly from the author of the updated textbook by the prestigious Springer publishers.
* amazon.com Book Review for the 1st Ed.:
Very good Non-Symbolic AI Overview and Introduction.
If you are searching for an overview of Non-Symbolic AI fields with introduction (not just shallow), hints and examples for practical application, and comparison of performance, strengths and weaknesses, this is definitely a book to be considered. It covers the topics pretty well, . . . .
Dr. Munakata has been active in the areas of artificial intelligence, new computing paradigms and analysis of algorithms. He is the author of two books – the textbook of this course and Matrices and Linear Programming with Applications, Holden‑Day, 1979. In addition to his research publications and the books, he served as the Guest Editor of four Special Issues for the Communications of the ACM, a premier magazine for 86,000 professionals worldwide (March 1994, Nov. 1995, Nov. 1999, and Sept., 2007). He also served as an Associate Editor, the IEEE Expert (now IEEE Intelligent Systems).