690 Design of Intelligent Systems
Course Description
This course discusses advanced concepts of
self-organizing networks of sparsely connected processing components (neurons).
Neural-net implementations of pattern recognition algorithms provide important,
practical advantages by allowing fast realization of parallel, iterative
procedures. Operations of self-organizing neural networks will be developed and
used for different neural functions. These functions will include implementation
of associative memories, statistical self-organization and learning, and
self-organization for the reinforcement lerning. An example self-organizing
neural system simulating biological systems will be examined. The majority of
biological intelligence creatures are simple, yet they can achieve complex
information processing and computational tasks that current artificial
intelligence cannot match. Can we use these simple models to learn how to
design better artificial intelligence?
The emphasis in this course is on development of the concept of
self-organizing neural system with locally interconnected processing
components. Students will simulate neural networks for patter recognition
and classification using PC software tools. This course will prepare you to
study computational principles and hardware organization of what we mean by
intelligence and goal-directed behavior. How to motivate machine to act on
its own, yet to satisfy a desired objective? How machine interaction with
environment leads to better behavior, better understanding, and success in its
mission? What are the computational and hardware issues in doing this
efficiently and in realtime? We try to define what it means to be intelligent,
anticipate, learn from experience, make associations, perceive, act
independently, self evaluate and think.
Syllabus
Schedule
Resources
Grades
Reference
Books
Related links
Reinforcement Learning Repository
University of Massachusetts, Amherst
USC Brain Theory and Artificial
Intelligence CS 564 : Fall 2001
Bio-inspired
Computing AI 23
Introduction to
Reinforcement learning
Neural Nets by
Kevin Gurney
Michael Arbib
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