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
Computer
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*.
ISBN: 978-1-84628-838-8

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, . . . .
Instructor
Profile:
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).