Work through the following materials this week.

• Sebastian Thrun’s introduction to unsupervised learning videos, Unit 6, found at on-line AI class, covers K-Means and Expectation Maximization (EM). Note:

  • Watch videos 1, "Unsupervised Learning", through 20, “Clustering Summary”, paying particular attention to k-Means and Expectation Maximization (EM).
  • Skim over the calculus, but take note of its value in practical applications of AI.
  • The text doesn’t include a discussion of k-Means, but Wikipedia has this entry: k-Means clustering.

• Chapter 20 in the text also covers similar content, so you can read through it for supporting material. Focus on the following material.

  • Section 20.1 - a general overview
  • Section 20.2.3 - a discussion Gaussian mixtures
  • Section 20.3 introduction and 20.3.1 - EM for Gaussians, the focus of our unit
  • Section 20.3.4 - Summary of generalized use of EM