Mathematical Foundations of Machine Learning

Module 1. This class focuses on the mathematical foundations of data science with a dual focus on linear algebra and probability. The linear algebra component will cover vectors, matrix, tensor, multiplication, inverse, determinant, trace, and norms (L1, L2, etc.). The probability component will cover random variables, distributions, expectation, marginal/conditional probability, independence, and correlation.