Independent Study Class

Objective

The goal of this course is to review some advanced topics in machine learning following "".

Room and Time

453-114 Wednesday 9:00am-11:00am

Schedule

 Session        Book section     Additional material
Sept 181. Introduction
2. Probability
 
Sept 253. Generative models for discrete data
3.1 Introduction
3.2 Bayesian concept learning
3.3 The beta-binomial model
 
Oct 23.4 The Dirichlet-multinomial model
3.5 Naive Bayes classifiers
 
Oct 94. Gaussian models
4.1 Introduction
4.2 Gaussian discriminant analysis
 
Oct 16-304.3 Inference in jointly Gaussian distributions
 
Nov 6 -134.4 Linear Gaussian systems
4.6 Inferring the parameters of an MVN
 
Nov 18-295.2 Summarizing posterior distributions
5.3 Bayesian model selection
 

Bibliography

  • Murphy, Kevin P. Machine learning: a probabilistic perspective. The MIT Press, 2012. (book site)

Resources