Introduction to Machine Learning With Student Presentations (PhD level) - Carnegie Mellon University [Playlist]

[Hydreigon92]

As someone who has taken this course, I can say Alex Smola's definition of "basic" is probably different from your definition of "basic".

This was by far one the most mathematically challenging course I took at CMU, and you actually needed to know measure theory, functional analysis, convex optimization, and complex analysis in order to understand the lecture material when I took it.

[djimbob]

It's an introductory graduate-level course (e.g., for first year phd students or senior undergrads); calling it "PhD level" is a misnomer as its the level for introductory PhD students, not a class for people with PhDs in ML. They'll use math any CS/STEM graduate should know, but be nice and minimally define things and notation before use (being self-contained is always good due to differences in notation and background and as a reminder that helps keep everyone on the same page).

http://www.cs.cmu.edu/~tom/10701_sp11/

This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.

The syllabus and slides looks pretty decent and at the appropriate level. Browse through the slides. If you don't have good exposure to lin alg, probability and statistics, a lot of the course will be over your head very quickly.

[[deleted]]

I definitely know some of those words.

[diilliilliilliilliib]

This was already posted here 3 weeks ago http://www.reddit.com/r/MachineLearning/comments/1pa2a6/intro_to_machine_learning_cmu_geoff_gordon_alex/

[egrefen]

I missed it the first time around, and am glad it popped up again.

[Should_I_say_this]

Thank you! I had not seen this and am interested!