CS 420: Advanced Topics in Machine Intelligence

Spring 2001: Natural Computation

Instructor:
Bruce MacLennan
Phone: 974-5067
Office: 217 Claxton Complex
Hours: 2:00-3:30 MW, 4:15-5:15 F, or make an appointment
Email: maclennan@cs.utk.edu

Teaching Assistant:
Chris Symons
Phone: TBA
Office: Cl 110B
Hours: Mon. 12:30-1:30, Tues. 1:00-3:00
Email: symons@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-S01

Information

Description

CS 420 covers advanced topics in machine intelligence with an emphasis on faculty research. In the Spring semester of 2001 the topic will be natural computation.

This course will be based on a new text, Dana H. Ballard's An Introduction to Natural Computation (MIT Press, 1999).

Natural computation is the study of the computational processes in natural systems, especially the learning algorithms found in nature. These algorithms operate on at least three timescales: (1) learning to react (neural networks), (2) learning during a lifetime (reinforcement learning), and (3) learning across generations (genetic algorithms & genetic programming). This course will present these three approaches to learning and the essential ideas they have in common.

Prerequisites

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

Subject to change! I currently anticipate that there will be one homework assignment per week, mostly exercises from the book. A few of these (probably 3 or 4) will be small programming projects, which you may implement in any reasonable programming language. Generally I will assign homework on Wednesday to be turned in the following Monday, but you will have more time for the programming projects.

At this time I do not anticipate having a final exam, so your grade will be based on your homework. Of course, all homework should be your own work.

Text

Dana H. Ballard, An Introduction to Natural Computation (MIT Press, 1997)

Tentative List of Topics

  1. Natural Computation (overview)
  2. Fitness (probability distributions, information theory etc.)
  3. Programs (search etc.)
  4. Data (compression, representation etc.)
  5. Dynamics (linear and nonlinear systems)
  6. Optimization (minimization, optimal control)
  7. Content-addressable Memories (Hopfield, Kanerva, RBF etc.)
  8. Supervised Learning (perceptrons, back-propagation etc.)
  9. Unsupervised Learning (PCA, competitive learning etc.)
  10. Markov Models (regular chains, nonregular chains, hidden M.M.)
  11. Reinforcement Learning (Markov decision process, Q-learning etc.)
  12. Genetic Algorithms (schemata, coevolution)
  13. Genetic Programming (operators, programming, modules)
We will do about one topic per week.
Return to MacLennan's home page

Send mail to Bruce MacLennan / MacLennan@cs.utk.edu
Last updated: Wed Apr 11 13:42:20 EDT 2001