CS 420/594: Advanced Topics in Machine Intelligence

Fall 2002: Complex Systems and Self-Organization

Grades are available here





Instructor:
Bruce MacLennan
Phone: 974-5067
Office: 217 Claxton Complex
Hours: 2:00-3:30 MW, or make an appointment
Email: maclennan@cs.utk.edu

Teaching Assistant:
William E Duncan
Phone: 974-0995
Office: Claxton 224
Hours: T/TH 2:35-3:35p
Email: duncan@cs.utk.edu

Classes: 3:40-4:55 MW in Cl 205

Directory of Handouts, Labs, etc.

This page: http://www.cs.utk.edu/~mclennan/Classes/420-594-F02


Information


Description

CS 420 covers advanced topics in machine intelligence with an emphasis on faculty research; CS 594 is similarly focused on faculty research. In the Fall semester of 2002 the topic for CS 420/594 will be complex systems and self-organization.

In emergent computation, information processing emerges from the parallel interaction of large numbers of comparatively simple computational units. Emergent computation is increasingly important as we seek to increase the power and robustness of computational systems by increased use of parallelism and by the exploitation of innovative computational technologies (optical, molecular, biological, etc.). Fundamental to the theory and implementation of emergent computation systems is an understanding of the behavior and self-organization of complex systems: systems in which the interaction of the components is not simply reducible to the properties of the components. This project-oriented course will focus on natural and artificial complex systems, including neural networks, cellular automata, and evolutionary systems.


Prerequisites

To be determined, but I anticipate the following:

 Basic calculus through differential equations (e.g. Mat 231, 241), linear algebra (e.g. Mat 251), probability and statistics (e.g. Mat 323). Note! Take these prerequisites seriously! You will need these skills to understand the material, to do the homework and to do the projects.


Grading

To be determined.
Students taking CS 594 (i.e. the course for graduate credit) will be expected to do specified additional work.
Subject to change!

Text

Bar-Yam, Yaneer.  Dynamics of Complex Systems.  Perseus, 1997. This book is available online in pdf format.

Tentative List of Topics

  1. Overview (course description, the complex systems field, complex systems, emergence, complexity, methods)
  2. Neural Networks I (attractor net, feedforward nets, subdivided nets)
  3. Neural Networks II (brain function & models of mind)
  4. Life I: Evolution (origins of complex organisms, exploration, optimization, selection)
  5. Life II: Developmental Biology (differentiation, mathematical theories, self-organization, pattern formation)
  6. Human Civilization I (defining & measuring complexity)
  7. Human Civilization II (human civilization as a complex system)
  8.  

    As time permits:

  9. Protein Folding I (size scaling, parallel processing, homogeneous & inhomogeneous systems)
  10. Protein Folding II (polymer dynamics)
We will do about one topic every two weeks. 

Projects/Assignments


Online Resources


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Send mail to Bruce MacLennan / MacLennan@cs.utk.edu

Last updated:  Sat Dec 14 16:40:33 EST 2002