Machine learning: Introduction

Math 2805: Mathematical principles of machine learning #

Prof. Thomas Pietraho
Bowdoin College

Modern machine learning lies at the confluence statistics and probability, computer science, and mathematics, evolving through a rich interplay among the three fields. The main focus of the class it to explore the mathematical structures and techniques that inform this conversation, but we will not be able to insulate ourselves from the other two essential components: working with data with its inherent uncertainty and its analysis using algorithms from computer science.

A selection of images from the course

Course description: An introduction to the mathematical theory and practice of machine learning. Supervised and unsupervised learning, with topics including regression, classification, clustering, dimension reduction, data visualization, denoising, norms and loss functions, neural networks, optimization, universal approximation theorems, and algorithmic fairness. Class will be lab and project-based, but no formal programming experience is necessary.