graphical models

Stanford's free online Probabilistic Graphical Models course

Just a very quick note to say I'm a week into Stanford's free online Probabilistic Graphical Models course.  It's really, really good and I'm learning loads (although does require a fair amount of work).  The online course covers the same content as Stanford's postgraduate PGM course (it's not watered down like Stanford's free online Machine Learning course) and has interesting programming assignments.  Very juicy stuff and it should substantially improve my ability to refine and implement some of my hand-wavy ideas.

This is the first on-line course I've taken and I'm very impressed.  It seems to be a near-perfect mix of the best bits from "real" lectures and the best bits from studying alone with a text book.  i.e. it's engaging and "human" like a lecture; but you also have the option to pause / rewind (like reading a text book) to think things through.

Free Stanford course on Probabilistic Graphical Models

One of the main research directions for my PhD is likely to be experimenting with bispoke probabilistic graphical models for representing multi-state appliances like washing machines.  As such, I need to learn about existing probabilistic graphical models.  For the past few days I've been reading a textbook called "Probabilistic Graphical Models" by Koller and Friedman.  So far I've really enjoyed the book.

Over lunch today, a friend of mine told me that Stanford are running a free on-line course on Probabilistic Graphical Models, presented by Koller. I've signed up - it looks like a great course; it starts in Feb. Stanford are also running a course on Information Theory, which I've also signed up for.

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