Video Introduction by Professor Strang

Course Meeting Times

Lectures: 3 sessions / week, 1 hour / session


18.06 Linear Algebra


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.


Strang, Gilbert. Linear Algebra and Learning from DataWellesley-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.

Course Info

As Taught In
Spring 2018
Learning Resource Types
Lecture Videos
Problem Sets
Instructor Insights