Machine Learning
Home
Google Group
HW submission
Project
Extra credit
Units
1 Introduction
2 Basic Tools
3 Naive Bayes
4 Perceptron
5 Optimization
6 Duality
7 SVM
8 Kernel Methods
9 Tail Bounds, Averages
10 Bootstrap
11 Graphical Models
Recitations
1 Linear Algebra
2 Probability
3 Kernels
4 Optimization
5 Duality & SVM
7 Tail Bounds & Averages
8 Learning Theory
9 Information Theory
10 Graphical Models
CMU
Geoff Gordon
Alex Smola
Carlton Downey
Ahmed Hefny
Dougal Sutherland
Leila Wehbe
Jing Xiang
MLD
CMU
Graphical models review
Introduction to Machine Learning - 10-701
Video
Supplementary material
Slides available
in PDF
.
For more on topic models, see for example
this survey
.