I have written a few small tutorial notes on various topics that were of interest to me. You can get them below. Please send me any comments or corrections you have!

NB: Until I get around to making progress on my barely started book, these are the best I've got to offer. But some are quite old, and none have been carefully checked over, so use at your own risk.

Also, I would be remiss if I didn't point you to

  • Kevin Murphy, who has many excellent tutorials and research notes on his page
  • Thomas Minka who is seemingly unstoppable and will fill you in on matrix calculus all things Bayesian
  • Adam Berger who has some good introductory notes on MaxEnt modeling
  • Other pages listing matrix identities:
    [ Imperial, Resa, CMU, Utah]

Possibly Useful Notes

  • Useful Matrix and Gaussian formulae
    Two "cheat-sheets" of useful matrix and Gaussian formulae for anyone who needs to take inverses or derivatives or conditional expectations using linear operators and normal densities. (Recently fixed an often reported error in eq 4c; in equation 3e the X^{-1} terms on the right should be transposed.)
    Matrix Identities     [revised june 99-->ps.gz(27K) pdf(122K)]
    Gaussian Identities [revised july 99-->ps.gz(24K) pdf(90K)]

  • Speech processing background
    A short tutorial on speech processing methods and terminology for those getting started in recognition, speaker id, synthesis, etc. (this became Appendix A in my thesis).
    [version of nov.98-->ps.gz (letter)(313K) ps.gz (A4)(313K)]


  • Graphical Models, HMMs, Linear Dynamical Systems, Unsupervised Learning, SVMs and VC Dimension
  • See my tutorials page for the notes

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Sam Roweis, Vision, Learning and Graphics Group, NYU,