3 ms·

If you know basic undergrad probability/statistics/linear algebra (matrices, vectors, eigenvalues) you can do CMU's graduate course in ML that is intended to pr

If you know basic undergrad probability/statistics/linear algebra (matrices, vectors, eigenvalues) you can do CMU's graduate course in ML that is intended to prepare PhD students to understand research papers in the field (Includes recorded lectures) https://sites.google.com/site/10715advancedmlintro2017f/lectures https://sites.google.com/site/10715advancedmlintro2017f/lect... CMU also has an 'Applied Machine Learning' undergrad course that is paywalled fully unfortunately, but they use the text: Witten, I. H. & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, second edition

cbHXBY1D 8y ago
And what about after that? In my experience at least, I can highly recommend Stanford's EE364a Convex Optimization. http://web.stanford.edu/class/ee364a/ http://web.stanford.edu/class/ee364a/ When I was an undergrad at Berkeley, one particularly well known research lab would only look at your resume if you took that course online. Warning: that class is not for the faint of heart. And be good at linear algebra.
- hackermailman 8y ago
  CMU also has a convex optimization course w/open recorded lectures on youtube http://www.stat.cmu.edu/~ryantibs/convexopt/ http://www.stat.cmu.edu/~ryantibs/convexopt/ None of these classes are for the feint of heart if you're actually enrolled in them and operating under crazy deadlines with thousands of dollars on the line. This is clearly for our casual interest and hacking around with ML research hobby.
- graycat 8y ago
  Stephen Boyd's Convex Optimization, http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf is gorgeous material. Large parts it are from theorems of the alternative, linear programming, Jensen's inequality, R. T. Rockafellar, etc., but Boyd makes it all a clean whole. That's the upside! The downside is that, IMHO, rarely does any one person need more than a small part of the whole book, and the parts that someone needs are likely also available in the subject they are working with. So, at least download Boyd's book and use it for reference: If ever have a question about convexity, then start with Boyd!
  - davidsrosenberg 8y ago
    Funny - I had the same thoughts and Boyd and Vandenberghe’s book, which is why I compiled this “extreme abridgment” for what you need for the class: https://davidrosenberg.github.io/mlcourse/Notes/convex-optimization.pdf https://davidrosenberg.github.io/mlcourse/Notes/convex-optim...
    - iampno 8y ago
      Nice
- throwawaymath 8y ago
  Which research lab was that?
colmvp 8y ago
Alas, it seems that they don't have videos for all their lectures?

cbHXBY1D 8y ago

And what about after that? In my experience at least, I can highly recommend Stanford's EE364a Convex Optimization. http://web.stanford.edu/class/ee364a/ http://web.stanford.edu/class/ee364a/ When I was an undergrad at Berkeley, one particularly well known research lab would only look at your resume if you took that course online. Warning: that class is not for the faint of heart. And be good at linear algebra.

hackermailman 8y ago
CMU also has a convex optimization course w/open recorded lectures on youtube http://www.stat.cmu.edu/~ryantibs/convexopt/ http://www.stat.cmu.edu/~ryantibs/convexopt/ None of these classes are for the feint of heart if you're actually enrolled in them and operating under crazy deadlines with thousands of dollars on the line. This is clearly for our casual interest and hacking around with ML research hobby.
graycat 8y ago
Stephen Boyd's Convex Optimization, http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf http://stanford.edu/~boyd/cvxbook/bv_cvxbook.pdf is gorgeous material. Large parts it are from theorems of the alternative, linear programming, Jensen's inequality, R. T. Rockafellar, etc., but Boyd makes it all a clean whole. That's the upside! The downside is that, IMHO, rarely does any one person need more than a small part of the whole book, and the parts that someone needs are likely also available in the subject they are working with. So, at least download Boyd's book and use it for reference: If ever have a question about convexity, then start with Boyd!
- davidsrosenberg 8y ago
  Funny - I had the same thoughts and Boyd and Vandenberghe’s book, which is why I compiled this “extreme abridgment” for what you need for the class: https://davidrosenberg.github.io/mlcourse/Notes/convex-optimization.pdf https://davidrosenberg.github.io/mlcourse/Notes/convex-optim...
  - iampno 8y ago
    Nice
throwawaymath 8y ago
Which research lab was that?

colmvp 8y ago

Alas, it seems that they don't have videos for all their lectures?