Mathematics for Artificial Intelligence and Machine Learning

Harvard Extension School

MATH E-142

Section 1

CRN 27005

View Course Details
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.

Instructor Info

Kris Lokere, ALM


Meeting Info

M 6:30pm - 8:30pm (1/26 - 5/16)

Participation Option: Online Synchronous

Deadlines

Last day to register: January 22, 2026

Prerequisites

At least two of the following courses or their equivalent: MATH E-21a, MATH E-23a, and STAT E-150.

Notes

This course meets via web conference. Students must attend and participate at the scheduled meeting time. See minimum technology requirements.

All Sections of this Course

CRN Section # Participation Option(s) Instructor Section Status Meets Term Dates
27005 1 Online Synchronous Kristiaan Lokere Open M 6:30pm - 8:30pm
Jan 26 to May 16