11-691 Mathematical Foundations for Data Science

12 units, Carnegie Mellon University

In this course we will cover the basics of Linear Algebra, and Probability and Statistics. The course is intended to be a refresher. Most Machine Learning courses expect students to have some background in these two topics. I expect that some of you will have taken one or more courses in each of these topics. I also expect that many, if not most of you who did have also forgotten much of what you studied. For others, one or more of these topics will be new. The aim of this course is to get you quickly up to speed in these areas. The course will be very fast paced. We will, in essence, be covering two semesters worth of material in one semester. We will cover each of the two topics for roughly six weeks. The lectures will only skim over the topics. You are expected to fill in the extra material yourself by studying the suggested textbooks.

Prerequisites: none listed

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