CS 7641 & 4641
Machine Learning


  1. Supervised Learning

  2. Randomized Optimization

  3. Unsupervised Learning and Dimensionality Reduction

  4. Markov Decision Processes

Implementation resources:

For the most part you should be able to find what you need for the assignments by searching the web, but in case you need it, here are some starting points.

Supplemental Reading:

  1. Support Vector Machines and Kernel Methods (all taken from www.kernel-machines.org's tutorial page )

    1. Burges' tutorial on Support Vector Machines (risk, and VC dimension) (ps)
    2. Burbidge & Buxton's Introduction to SVMs (pdf). Some consider this a gentler introduction to SVMs.
    3. Scholkopf's NIPS tutorial slides on SVMs and kernel methods ps)
  2. Boosting

    1. Schapire's Introduction to Boosting (ps)
    2. Boosting and margins (pdf)
  3. Information Theory (pdf)

  4. Randomized Search/MIMIC (pdf)

  5. Berkhin's clustering survey (pdf)

  6. Kleinberg's NIPS 2002 impossibility result for clustering (pdf)

  7. Fodor's survey of dimensionality reduction techniques (pdf)

  8. Reinforcement Learning
    1. Kaelbling, Littman and Moore's survey of reinforcement learning (pdf)
    2. Reinforcement Learning: An Introduction, by Richard S. Sutton and Andrew G. Barto. Check out the link to the HTML version at the bottom of the intro page.
    3. Michael Littman's slides on RL. In particular, the last slide contains the simplified TD-lambda update rule we covered in class.
  9. Andrew Moore's tutorial slides on zero-sum game theory and non-zero-sum game theory.