Saturday, October 08, 2011

Stanford Machine Learning class

Stanford is offering a free online version of it's Machine Learning class taught by Andrew Ng. Study groups are popping up everywhere. Cool!

The class officially starts Monday, October 10th, but the first few lectures are up already, broken into bite size pieces of 10 minutes or so. What I've seen so far is at a basic level, covering a course introduction and terminology. Ng then posses a linear regression problem.

We want to find a line y = ϴ0 + ϴ1 x such that we minimize the squared error between our line and our data points.

The solution is our first learning algorithm, gradient descent.

More about the Machine Learning class

The real class at Stanford is: CS229. Exercises are to be done in Octave. Recommended reading includes the usual suspects:

  • Pattern Recognition and Machine Learning, Christopher Bishop
  • Machine Learning, Tom Mitchell
  • The Elements of Statistical Learning, Hastie, Tibshirani and Friedman

Several of the Primers in Computational Biology series would probably make for good supplementary material.

There are threads related to the class on Quora and Reddit, for whatever that's worth. Also, see some good resources for learning about machine learning.