A graduate-level introduction to machine learning at Carnegie Mellon, designed to prepare PhD students to read research papers in the field. Lectures were recorded and posted for the 2017 offering.

Commenters point to it as a graduate ML course that is accessible with undergraduate probability, statistics, and linear algebra, and note that its recorded lectures are available.

What commenters said (1)

1. Recommendshackermailman2018in “Foundations Machine Learning”

   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/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

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