Monte Carlo Methods, Stochastic Optimization

I heartily recommend the notebooks published in this course as excellent applied reference material to estimation and optimization.

I love it how code and coursework are intermingled, reminiscing me of Knuth's Literate Programming [1]

My beef with many other courses offered (including Coursera) is that they use Matlab when it's clearly advantageous to use IPython Notebook as a better experimenting environment. For example, Daphne Koeller's PGM course[2] is still in Matlab and no matter what you do the code looks extremely clumsy and hard to read. N.B. I wrote tens of thousands of lines of Matlab code, including GUI programs, but that does not mean it's a good language to use especially in cases like this.

[1] http://en.wikipedia.org/wiki/Literate_programming

[2] https://www.coursera.org/course/pgm

I took this class when I was in graduate school at Harvard. The methods introduced in this course can be used in virtually every domain. I think everyone involved with some sort of data processing should have at least minimal knowledge of Bayesian inference, simulated annealing, data augmentation, sampling, etc...

Just covered Monte Carlo and stochastic topics in MIT 6.00.2x on edX. Cool stuff!

are there download links for the videos somewhere? I don't have flash installed and need to travel tomorrow ;)

I bookmarked this for a later date. I am currently taking a Coursera course and one of the projects touched on Monte Carlo methods, they are super cool, I implemented a rudimentary AI that plays tic-tac-toe(well) in like 70 lines of Python.