Recent courses – Stanford ML & PGM
April 8, 2012 2 Comments
Preparing for our 2012-2014 plan to develop a few machine learning algorithms for the Arduino platform, I’ve taken (taking) advantage of a few of Stanford University’s free sponsored advanced CS courses. During the last quarter of 2011, I completed the ML course and now in the first quarter of 2012, I’m taking the PGM course. The ML course was awesome and really solidified my understanding of kernel based regression learning. Professor Andrew Ng‘s teaching style was excellent and I have all of the videos and lectures for reference. I’m currently taking Professor Daphne Koller’s PGM course covering the two main classes of Probabilistic Graphical Models … Bayesian and Markov Networks. I’ve got to warn you that the PGM course is a bit more difficult but well worth the time.
All of these courses are through Coursera.
Additionally, If you’re interested in a great introduction to ML and PGM, mathematicalmonk has several excellent introductions to these very interesting (but advanced) concepts on YouTube.
You also don’t need to be intimidated by the advanced nature of these courses. If you’ve had a little exposure to linear algebra, you’ll have no problem.
I would also recommend Khan Academy for a refresher on probability since all of these methods utilize probability in one form or onother.