Web Sites for Teaching Undergraduate Mathematics of Computation
(Except numerical analysis)

The following contain many postscript lecture notes and exercises in Algorithms (and Data Structures):

1. by Ming Li at University of Waterloo.
2. by John Morris at University of Western Australia.
3. by Phil Rogaway at UC Davis - also has Modern Cryptography.
4. by Robert Sedgewick at Princeton University.

The popular (1986-94) book, Algorithms and Complexity, by Herbert Wilf, is now available in postscript at no cost. After some preliminaries, its chapters are: Recursive algorithms, Network flow problem, Algorithms in the theory of numbers, and NP-completeness.

Applets for Neural Networks and Artificial Life, by Akio Utsugi at National Institute of Bioscience and Human-Technology (Japan)

This has a collection of Java Applets, mostly contributed by others, with links to related sources. Besides neural nets and artificial life, the author has found related applets for simulated annealing, cellular automata, and more.

Artificial intelligence, Dave Marshall at Cardiff University.

This has notes and exercises in HTML (you won't be able to donwload his postscript files, but you could e-mail him). Subjects include search, knowledge representations, represenations of uncertainty, planning, learning and vision.

Artificial Neural Networks Technology, by Department of Defense.

This is a book-structured set of notes that begins with history and goes through the most basic subjects. The coverage is at an overview level, so its value is to give the student the "big picture". The complete set of notes can be downloaded as a postscript file. A list of such tutorials can be found at http://www.kcl.ac.uk/neuronet/intro/index.html, by the Foundation for Neural Networks.

Designing and Building Parallel Programs, by Ian Foster at Argonne National Laboratory.

This is a book, written in HTML (full version published by Addison-Wesley in 1995). The web version is succinct, but complete,

Free On-Line Dictionary of Computing, by Denis Howe at Imperial College.

This has evolved into the premier dictionary on computer science.

History of Computing, by J.A.N. Lee at Virginia Tech.

This is a modest collection of items created and maintained by a premier computer scientist. Besides the ususal notes and links to founders, like Turing, there is also a history of the World Wide Web. If you use history in your class, you might also want to bookmark Pioneers of Computing, from The Virtual Museum of Computing.

Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto at University of Massachusetts.

This was published by MIT Press, but its early version still on the web. This can be considered as an application of Markov decision processes, and dynamic programming is one of the methods described. Other methods are Monte Carlo and Temporal Difference Learning (relatively new). Part III gives a "Unified View" that includes DP, MP and TDL as special cases.

CIspace: Tools for learning Computational Intelligence, by Leslie Tung, Kevin O'Neill, Mike Cline, Alan Mackworth and David Poole at University of British Columbia.

This contains three Java appletts: Graph Searching, Constraint Satisfaction Problem Solving, and Belief Network Inference. It is designed to be used with the author's book, but it can be used independently.

Turing Machines, by Suzanne Britton (no affiliation).

Provides brief introducition to TM and the Busy Beaver problem. A Java applet lets student enter code for a simple TM and run it. If your students have access to MS Windows environment, they might want to download WinTur, by S. Brandon Keller at University of Maine (student project).

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