# 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