10-600 Mathematical background for Machine Learning

12 units, Carnegie Mellon University

This course provides a place for students to practice the necessary mathematical background for further study in machine learning particularly for taking 10601 and 10701. Topics covered include probability, linear algebra inner product spaces, linear operators, multivariate differential calculus, optimization, and likelihood functions. The course assumes some background in each of the above, but will review and give practice in each. It does not provide fromscratch coverage of all of the above, which would be impossible in a course of this length. Some coding will be required the course will provide practice with translating the above mathematical concepts into concrete programs. This course supersedes the two minicourses 10606 and 10607.

Prerequisites: none listed

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