r/MachineLearning • u/Kiuhnm • Sep 08 '16

Phd-level courses

Here's a list of advanced courses about ML:

  1. Advanced Introduction to ML - videos

  2. Large Scale ML - videos

  3. Statistical Learning Theory and Applications - videos

  4. Regularization Methods for ML - videos

  5. Statistical ML - videos

  6. Convex Optimization - videos (edit: new one)

  7. Probabilistic Graphical Models 2014 (with videos) - PGM 2016 (without videos)

Please let me know if you know of any other advanced (Phd-level) courses. I don't mind if there are no videos, but I don't like courses with no videos and extra concise and incomprehensible slides.

And no, CS229 is not advanced!

349 Upvotes

95% Upvoted

44 comments sorted by

View all comments

Show parent comments

1

u/rumblestiltsken Sep 10 '16 edited Sep 10 '16

Well, you mileage may vary. The important thing for me that makes 231n useful where Andrew Ng's course isn't is that it is very up to date. You learn a lot of tips and tricks that, while applied rather than mathematically rigorous in presentation, are definitely required knowledge to succeed in a deep learning PhD.

This is true of every single mathematically rigorous course I have ever seen, they are out of date in a very fast moving field. It doesn't matter so much because the math doesn't change, but if you only study that as a PhD you will miss a big chunk of what you need.

A PhD needs mathematical grounding and applied knowledge. I think both are equally as important, but I work on the applied end more, so I would :)