CS 461: Machine Learning
Instructor: Kiri Wagstaff

Reading Questions for Lecture 1

Introduction / Machine Learning (Ch. 1)
  1. Classification: What is a discriminant?
  2. Regression: to train an autonomous car to predict what angle to turn the steering wheel, where could the training data come from?
Supervised Learning (Ch. 2.1, 2.4-2.9)
  1. Is the most specific hypothesis S a member of the version space? Why or why not?
  2. What happens if the true concept C is not in the version space?
  3. What is Occam's Razor?
  4. (Grady) What is a version space?
  5. (Grady) How do you get Empirical Error?
  6. (Jillian) With regards to hypotheses classes, what defines a case of doubt and why do we have doubt?<