Advices about reading/presenting/writing papers:

  1. How to Get Your SIGGRAPH Paper Rejected, Jim Kajiya, SIGGRAPH 1993 Papers Chair. [link]
  2. "How to Read a Research Paper" by Spencer Rugaber. [link]
  3. "How to Present a Paper" by Ashwin Ram. [link]
  4. "You and Your Research," Dr. Richard W. Hamming, March 7, 1986. [link]
  5. Prof. David Patterson's talks on career in research and academia. [link]
  6. more resources here

Matlab:

Matlab is recommended for programming and demonstration in this class although use of other languages like Java or C++ is also welcome. The Unix workstations in the AcIS Engineering Terrace (215 Mudd) are installed with Matlab. You may run the Matlab program on AcIS CUNIX machines by using "ssh -l user-name cunix.cc.columbia.edu" and then run 'matlab'. The program resides on /opt/local/bin/matlab'. More information about Matlab license on CUNIX machines is available at CUNIX Matlab site.

Students interested in taking the MATLAB course may consider the following
COMS W3101, Section 004: PROGRAMMING LANGUAGES (MATLAB)

You can also buy a "student edition" of Matlab from the Mathworks. You will need Image Processing Toolbox and Statistics Toolbox in order to use many pre-defined functions. Make sure to check the version numbers of the software when searching for specific functions.

Software:

  1. Tutorials on Matlab
    1. excellent Matlab tutorial from Utah Geo department [link]
    2. Introducing Matlab [link]
    3. A demo of image processing in Matlab [link]
    4. Getting Started with Matlab, by the Mathworks. [link]
    5. UNH Matlab Tutorial [link]
    6. US Navy Matlab Tutorial [link]
    7. MTU Introduction to Matlab [link]
    8. Mathworks' Matlab documentation [link]
  2. Pattern Recognition Tools, PRTools [link]
  3. Kevin Murphy's software page [link]
  4. Netlab web site by Bishop and Nabney [link]
  5. spider machine learning toolbox, GNU software, [link]
  6. Bayesian Network Editor and Toolkit [link],
    a windows enviroment sdk with IDE-like development enviroment
  7. Chih-Chung Chang and Chih-Jen Lin, LIBSVM: a library for support vector machines [link]
  8. kernel machines resrouce site, the portal of SVM [link]
  9. Graphic Model Tool Kit
    Jeff Bilmes and Geoffrey Zweig, "The Graphical Models Toolkit: An Open Source Software System for Speech and Time-Series Processing," ICASSP 2002. [link]

Other Useful Resources:

  1. Prof. William Freeman's class on "Learning and Inferencing of Vision" [link] offered at MIT.
    Our class is modeled after the above one, with a different focus on video analysis/indexing. A list of papers and student presentations in Prof. Freeman's class is here. It includes examples of excellent presentations and computer examples.
  2. How to create your web site?
    see instructions at:
    http://www.columbia.edu/acis/webdev/create.html