Machine Learning

In this lecture, we will look at probabilistic criteria for defining what it means to learn. Specifically, we will see maximum a posteriori and maximum likelihood learning criteria with examples.

Lectures

• Introduction to Probabilistic learning

• The Bayes Optimal Classifier

• Generative and Discriminative Learning

• Video
  • Introduction to Probabilistic Learning
  • Maximum Likelihood Estimation for Linear Regression
  • Generative & Discriminative Learning
• Older videos
  • Introduction to Probabilistic learning
  • Two examples of Maximum Likelihood Estimation
  • Discriminative and Generative Learning

Links and Resources

• Chapter 6 of Tom Mitchell’s book

• Chapter 7 of A Course in Machine Learning by Hal Daume

• Chapters 2 and 3 of Pattern Classification by Duda, Hart and Stork