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I've been pretty busy lately following the wonderful machine learning Stanford open course of professor Andrew Ng.

When I learned about this Stanford initiative this summer I was really interested and decided to follow one course just to see how it was done. I chose machine learning over AI because I felt I had more to learn in that field, as a matter of fact I have followed a machine learning course in my final MSc year but never put what I learnt into practice and just remembered enough to impress a recruiter during an interview. I didn't consider following the database course, but I'm sure I'ld have learn more, but perhaps having less fun...

I'm roughly halfway through the course, here's my takeover for the moment.

It IS possible to deliver a compelling video course, the key aspects: short segments (~10 minutes), good video and audio quality, simple slides and colored doodles.

ml-class doodle

The quizzes, both those during the videos and the review questions, are very good to memorize stuffs.

The simple programming exercises debunk the complexity of the algorithms. The use of Octave (a free alternative to Matlab) allows you to focus on the maths not on technical time-consuming stuffs.

I like the way professor Ng puts the emphasis on the intuition of how the system works before he takes us for a tour of the machinery.

Neural networks back propagation is not a magical trick, it's actually a cost optimization relying on the cost gradient.

Anyway, I think I'd now be able to start tinkering with machine learning in some projects. I hope this experiment will be renewed, I'd like to take another class next semester!