First, finish lectures by Professor Gilbert Strang. http://web.mit.edu/18.06/www/ http://web.mit.edu/18.06/www/ To my memory, session notes of CS229 is good en

First, finish lectures by Professor Gilbert Strang. http://web.mit.edu/18.06/www/ http://web.mit.edu/18.06/www/ To my memory, session notes of CS229 is good enough for understanding SVM and gaussian distributions. Also watch youtube videos. http://www.stanford.edu/class/cs229/materials.html http://www.stanford.edu/class/cs229/materials.html http://www.youtube.com/watch?v=UzxYlbK2c7E http://www.youtube.com/watch?v=UzxYlbK2c7E If you just want to use the libraries, you can stop here. If you want to know more, read chapters 1-3 of nonlinear programming by Professor Dimitri Bertsekas before convex optimization. http://www.athenasc.com/nonlinbook.html http://www.athenasc.com/nonlinbook.html Then, you can try to finish EE364 and watch the videos. http://www.stanford.edu/class/ee364a/ http://www.stanford.edu/class/ee364a/ http://www.youtube.com/watch?v=McLq1hEq3UY http://www.youtube.com/watch?v=McLq1hEq3UY If you want to roll your own algorithms, you have to know some optimization tools. http://cvxr.com/cvx/ http://cvxr.com/cvx/ And there is some statistics knowledge you have to fill in. I used these: http://www.stat.umn.edu/geyer/5101/ http://www.stat.umn.edu/geyer/5101/ http://www.stat.umn.edu/geyer/5102/ http://www.stat.umn.edu/geyer/5102/ R is used in the courses.