CS 461: Machine Learning
Instructor: Kiri Wagstaff

Reading Questions for Lecture 5

Probability (Appendix A) and Bayesian Learning (Ch. 3.1, 3.2, 3.7, 3.9)
  1. (Herman) How is the "evidence" of a given variable or item computed (e.g., P(W) for probability of wet grass)? (See p. 49 in Alpaydin)
  2. (Sam) How can non-binary variables be modeled in a Bayesian Network?
  3. (Sassja) Would it be useful to simulate a Bayesian network with a neural network?