Teaching

Bayesian Statistics for Machine Learning, 2018 & 2019
Bachelor Kunstmatige Intelligentie, University of Amsterdam
Code: 5082PTFM6Y
Credits: 6EC
When: Semester 1, period 1

Each lecture is 6 hours of material.

• Lecture 1: Basic probability, probability spaces, PMFs, PDFs, joint and conditional probabilty, sum and product rule, Bayes’ theorem
  (slides)
• Lecture 2: Random variables, expectations, moments, common distributions, estimators
  (slides)
• Lecture 3: Manipulating random variables, moment generating functions, transformations of random variables
  (slides)
• Lecture 4: Maximum likelihood, gradient descent, the exponential family
  (slides)
• Lecture 5: Bayesian inference, The Bent Coin, conjugacy
  (slides)
• Lecture 6: Model comparison, the evidence, Bayesian linear regression, mixture models
  (slides)

UCL Machine Vision: 2016-2017
COMPGI14/COMPM054 | Michaelmas term