Mathematics for Machine Learning

The course contains multiple topics in calculus and multivariate calculus which are utilized in and are fundamental to many machine learning methods as: functions, gradients, derivatives, derivation rules, functions of many variables, partial derivatives, the Jacobian, multivariate chain rule and application to neural networks, approximation of functions, power series, multivariate Taylor series, optimization, gradient descent and regression.