Machine Learning
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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
Support Vector Classification
Introduction to Machine Learning - 10-701
Video
Also available
annotated
.
Also available
annotated
.
Slides
Lectures
9
(
annotated
) and
10
(
annotated
).