Video Introduction by Professor Strang
Course Meeting Times
Lectures: 3 sessions / week, 1 hour / session
Prerequisites
Description
Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.
Textbook
Strang, Gilbert. Linear Algebra and Learning from Data. Wellesley-Cambridge Press, 2019. ISBN: 9780692196380.
Professor Strang created a website for the book, including a link to the 
Table of Contents (PDF), sample chapters, and essays on Deep Learning (PDF) and Neural Nets (PDF).
Requirements and Grading
There are homework assignments, labs, and a final project. The grade is based on all three elements. NOTE to OCW USERS: The OCW site includes problems assigned for every lecture, aligned with readings in the course textbook. The on-campus students had weekly problem sets.