This is the home page for the course CS 256: Neural Computation, offered by the Department of Computer Science at the University of Vermont, Spring 2005.
(N.B., the content of this page changes frequently.)

General Information:

Catalogue description: Artificial neural networks, their computational capacity and limitations, and the algorithms used to train them. Statistical capacity, convergence theorems, reinforcement learning, generalization.

(Further details appear in the syllabus and course outline.)

• Class meets on Mondays, Wednesdays, and Fridays in Room 367 Votey, 3:35 – 4:25 p.m.
• Office Hours: Wednesdays, 1:30 – 3:00 p.m., and Fridays, 10:30 a.m. – 12:00 noon, in Room 353 Votey.

Handouts:

Most handouts are distributed in pdf format, a page description language supported by Adobe Acrobat. If you do not have Acrobat Reader for your personal computer, you can download it for free from Adobe.

• Syllabus [pdf] 1/12/2005, 2 pages.
• Course Outline [pdf] 1/12/2005, 5 pages.
• The latex template should generate this typeset document.

Reading Assignments:

• Weeks 1 & 2.
• Weeks 3 & 4.
• Weeks 7 & 8.
• Weeks 9 & 10.
• Weeks 11 & 12.

Homework Assignments:

• Homework 1 is due 02/04/05
• Homework 2 is due 02/23/05
• Project proposals are due on 03/04/05.
• Homework 3 (Problem 1 and Problem 2) are due 04/15/05. (See also test data for Problem 2.)
• The second take-home exam [pdf] is due 04/29/05. Click here for the data used in Problem 1.

Lecture Notes:

• Introduction [pdf] 19 Jan. 2005, 23 pages.
• McCulloch-Pitts Networks [pdf] 31 Jan. 2005.
• Hopfield Networks 1 [pdf] 25 Feb. 2005.
• Probability Theory [pdf] 25 Feb. 2005.
• Hopfield Networks 2 [pdf] 25 Feb. 2005.
• Perceptrons [pdf] 8 Mar. 2005.
• LMS Algorithm [pdf] 9 Mar. 2005.
• Backpropagation Algorithm [pdf] 1 Apr. 2005.
• Capacity of an LTU [pdf] 8 Apr. 2005.

External Resources:

• Bayesians Worldwide: an index of scholars of Bayes Theory.
• www.boosting.org, a compilation of research on boosting algorithms in machine learning.
• Caltech's Ph.D. program on Computation and Neuron Systems (CNS).
• CiteSeer: a search engine for publications in computer and information science.
• COLT: Computational Learning Theory
• John Hopfield's home page.
• IEEE Computational Intelligence Society supports the IEEE Transactions on Neural Networks (Search on IEEE XPlore)
• IEEE Information Theory Society supports the IEEE Transactions on Information Theory (Search on IEEE XPlore)
• International Neural Network Society (INNS).
• Journal of Machine Learning Research.
• Kernal-Machines.org.
• The LaTeX Project home page.
• Teuvo Kohonen's home page.
• Machine Learning, a journal published by Kluwer.
• MLNet: Machine Learning Network Online Information Service.
• Neural Computation, published by MIT Press.
• NeuroCOLT: Neural Networks and Computational Learning Theory
• Nils J. Nilsson's home page.
• NIPS and NIPS Online: A DjVu archive of Volumes 1–13 of Advances in Neural Information Processing Systems.
• RIKEN Brain Science Institute, Laboratory for Mathematical Neuroscience.
• Rich Sutton's web site and his 1988 paper on the temporal difference algorithm.
• Richard A. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction, MIT Press, Cambridge, MA, 1998.
• Gerald Tesauro, Temporal difference learning and TD-Gammon, Communications of the ACM, 38(3), 1995, pp. 58–68.
• A facsimile of Alan M. Turing's 1948 report, Intelligent Machinery. An annotated version appears in B. Jack Copeland, ed., The Essential Turing, Clarendon Press, Oxford, 2004, pp. 395 – 432.
• UCI Machine Learning Repository.
• Bernard Widrow's home page.