This course provides a place for students to practice the necessary mathematical background for further study in machine learning — particularly for taking 10-601 and 10-701. 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. Some coding will be required: the course will provide practice with translating the above mathematical concepts into concrete programs.

The course is split into two minis, which form a sequence (10-606 is a prerequisite for 10-607).