#282 overall
10-601 Machine Learning
Carnegie Mellon University
2 recommenders · 2 threads · recommended in 2 years, 2011–2018 · score 1.8
A Carnegie Mellon introductory machine learning course covering core ML methods and their math foundations, taught as a graduate-level course for senior undergraduates and graduate students. Lecture recordings from earlier offerings are available as YouTube playlists.
Commenters describe it as a rigorous, hard but worthwhile introduction to ML, and one suggests it as the first course to take.
Machine Learning & AIintermediatefreemixed#machine learning#carnegie mellon#graduate course#linear algebra#probability#rosenfeld
Recommendations by year
20062026
Machine Learning.
In 2018 I went from "zero to sixty", so to speak. At the beginning of the year I knew nothing about ML, and my math skills had gotten so rusty I couldn't even remember how to do simple derivatives or multiply two matrices.
In the first half of 2018 I relearned the college math that I had forgotten by working through MIT OCW's Linear Algebra, Calculus, and Probability courses.
In the Fall, I enrolled in Carnegie Mellon's 10-601 course, graduate "Introduction to Machine Learning". It was hard, it was a ton of work, but I did well and it was completely worth it as I now have a solid understanding of the basics of the field and can continue studying this upcoming year and into the future.
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/
There's a practical course for this http://www.datasciencecourse.org/lectures/ anything you don't know, like linear algebra, look up the topics here for a 1-2hr crash course https://www.youtube.com/playlist?list=PLm3J0oaFux3aafQm568bl...
There's a playlist for a math background in ML for anybody who wants to try a more rigorous ML course https://www.youtube.com/playlist?list=PL7y-1rk2cCsA339crwXMW... More information, including recommended texts https://canvas.cmu.edu/courses/603/assignments/syllabus but don't let that list of prereqs discourage you, can easily look them up directly. You don't have to understand all of Linear Algebra to do matrix multiplication. There's plenty of ML books, papers and playlists on youtube for a full course in ML from dozens of universities https://www.cs.cmu.edu/~roni/10601/ (click on 2017 lectures)
Note never trust YouTube or any other resource to be around forever, make sure you archive everything before you start taking it as lectures tend to disappear (then seed them for others ^^ )
If you have a really weak background go through this free book, refuse to not be able to complete it https://infinitedescent.xyz/
There's no answers because the author gives thanks to a grad course in evidenced based teaching where he claims the only way to really know something and remember it is to figure it out for yourself. Math stackexchange can help too.