Andrew Ng's notes are excellent but I think it'll be wrong to characterize them as "non-theory" since by lecture 8, he starts going into learning theory stuff s

Andrew Ng's notes are excellent but I think it'll be wrong to characterize them as "non-theory" since by lecture 8, he starts going into learning theory stuff such as empirical risk minimization, structural risk minimization, VC dimensions..etc. Even in his other notes, when he derives algorithms for regressions, SVMs, PCA..etc, they always include a healthy does of theory and math. In fact, I think this approach to learning the subject is far better than simply "implementing a few common algorithms" since you get a much better grasp of what the algorithm is doing and you will be able to apply them much much better once you understand some of the theory behind it. On the content of the cs229 itself, I think the course is overall excellent. His problems sets are good too since they are hard enough to be challenging yet its not completely impossible to solve them. However, the course is a little heavy on SVMs ( like.. 5 lectures on them? ) and also leaves out some of the more interesting topics in Machine learning such as graphical models, bayesian nets..etc. But, I'm really glad that they made this course available since there isn't an equivalent undergraduate course in machine learning at my school.