Syllabus

Video Introduction by Professor Strang

Course Meeting Times

Lectures: 3 sessions / week, 1 hour / session

Prerequisites

18.06 Linear Algebra

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 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

Learning Resource Types

theaters Lecture Videos
assignment Problem Sets
co_present Instructor Insights