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G.Brown

Additional Material
Everybody has a different way of learning.
The methods used in course may prove to be just your style, or not.
Therefore the best way to learn this stuff is to draw on a variety of sources.
There are lots of other Machine Learning courses in other Universities round the world.
Here are just a few that you may like to draw on  to give you a different perspective on the
material you will see in this course.
If you'd like to buy a book, I highly recommend
Introduction
to Machine Learning by Ethem
Alpaydin.
Other courses/tutorials
Support
Vector Machines Explained  By Tristan Fletcher
Ben Taskar's course at UPenn
Andrew Ng's course at Stanford University
Some of my favorite Papers
On Feature Selection, BiasVariance, and Bagging, by Art Munson and Rich Caruana,
PDF
On Discriminative vs Generative Classifiers: A Comparison of Logistic Regression and Naive Bayes,
by A. Ng, and M. Jordan,
PDF
A Stability Index for Feature Selection, by Ludmila Kuncheva,
PDF
Statistical Modelling: The Two Cultures, by Leo Breiman,
PDF
And finally... an interesting article on
building ML products,
PDF
Videos
Mathematics related videos :
Khan Academy's linear algebra videos
Khan Academy's probability theory videos
The Mathematical Monk on YouTube
Probability, Information Theory and Bayesian Inference
Basics of Probability and Statistics
General Videos
What is Machine Learning?
Embracing Uncertainty by Microsoft
Intro to Feature Selection
Ensemble Algorithms: Tutorial on "Boosting"
Machine Learning in Computer Vision
Machine Learning in Bioinformatics


