10-702 is Carnegie Mellon's graduate-level course on statistical machine learning, the theory-heavy follow-on to the introductory 10-701 course. Commenters point to it as a second course for students who want the statistical foundations of ML.

Commenters present it as the theory-focused next step after the intro course, suited to anyone wanting a solid grounding in the statistical side of ML.

What commenters said (2)

1. Recommendseykanal2016in “Statistical Machine Learning, Spring 2016”

   For those of you interested in this stuff, this course (10-702) is the second in a series. The 10-701 course, "Intro to Machine Learning", is a fantastic course as well, even if just for the exercises. This year's version is here [1], you can find lecture notes, links to lectures posted on YouTube, homeworks, readings, etc. You can also just google "10-701" and see a lot of previous course websites with similar material.

   For what it's worth, nowadays, the problem isn't availability of learning material. This stuff is being literally given away. Its that you, the student, has to really dedicate time to the material. The first homework assignment for the 10-701 class wasn't even that difficult (relatively speaking) and it still took me over ten hours to finish. Persevere! It's worth it.

   [1]: http://www.cs.cmu.edu/~mgormley/courses/10701-f16/

2. Recommendskubrickslair2011in “Ask HN: How do I start with Machine Learning?”

   Yes, you are correct. Sorry for the confusion; I meant that courses like 10-702 should be the star course he should work at, to have some short at understanding and contributing to theory heavy side of ML.

   The first basic course for the latter approach can be 10-601, it's a machine learning course meant for senior undergrads. Look at schedule and assignments.

   http://www.cs.cmu.edu/~roni/10601-s10/

   Also since you have interest in text mining etc. and if you want to focus on that, you can skip a lot of courses, and go directly to language and statistics-1. It's a great course if you wish to work primarily with text, and it covers the prerequisite ML.

   http://www.cs.cmu.edu/~roni/11761/

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