Lectures

You can download the lectures here. We will try to upload the annotated classroom slides after the corresponding classes.

• (foml-00) Course logistics
  tl;dr: Course logistics and organization.
  [slides]
  
• (foml-01) ML and Learning Paradigms
  tl;dr: What is Machine Learning and different learning paradigms.
  [slides]
  

  Suggested Reading

  • Chapter 1.0 and 1.1 from Bishop
  • Chapter 1.1 to 1.4 from UML book
• (foml-02) Probability Refresher - 1
  tl;dr: Probability, sum rule, product rule, random variable, distribution, marginal, conditional, Bayes rule.
  [slides] [annotated-slides]
  

  Suggested Reading

  • Chapter 1.2.0 and 1.2.1 from Bishop
• (foml-03) Probability Refresher - 2
  tl;dr: Expectation, Variance of a random variable and the Gausssian distribution.
  [slides] [annotated-slides]
  

  Suggested Reading

  • Chapter 1.2.2 and 1.2.4 from Bishop
• (foml-04) MLE
  tl;dr: Maximum likelihood principle - estimating the most likely explanation of the data.
  [slides] [annotated-slides]
  

  Suggested Reading

  • Chapter 1.2.3 to 1.2.5 from Bishop
• (foml-05) MAP and Fully Bayesian Treatment
  tl;dr: Maximum likelihood principle - estimating the most likely explanation of the data.
  [slides] [annotated-slides]
  

  Suggested Reading

  • Chapter 1.2.5 and 1.2.6 from Bishop
• (foml-06) Linear Regression with Basis functions
  tl;dr: Maximum likelihood principle - estimating the most likely explanation of the data.
  [slides] [annotated-slides]
  

  Suggested Reading

  • Chapter 3.1 from Bishop