Mathematics for Artificial Intelligence and Machine Learning

This course teaches the mathematics needed to understand how artificial intelligence (AI) works under the hood.

As machine learning becomes more ubiquitous and the software libraries easier to use, developers may become unaware of the underlying design decisions, and therefore the limitations and possible biases, of machine learning algorithms.

This course aims to bridge the gap between a thorough knowledge of mathematics and the machine learning methods that are based on it.

We start with an intensive review of concepts from linear algebra, analytic geometry, vector calculus, optimization, and probability, and then apply them in detail to machine learning methods such as regression, dimensionality reduction, density estimation with Gaussian mixture models, and classification with support vector machines.