Instructor Insights

Instructor Insights

In the following video and Chalk Radio podcast episode, Professor Gilbert Strang describes various aspects of how he taught 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning.

Curriculum Information

Prerequisites

18.06 Linear Algebra

Requirements Satisfied

18.065 can be applied toward a Bachelor of Science in Mathematics , but is not required.

Offered

Every spring semester

Assessment

Grade Breakdown

The grade for the course is based on homework assignments, labs, and a final project.

Student Information

Enrollment

54 students

Breakdown by Year

Both undergraduate and graduate students

Breakdown by Major

Variety of majors

How Student Time Was Spent

During an average week, students were expected to spend 12 hours on the course, roughly divided as follows:

Lecture

Met 3 times per week for 1 hour per session; 39 sessions total.

Out of Class

Students were expected to complete weekly problem sets, labs, and a final project.

Course Team Roles

Lead Instructor (Professor Strang)

Professor Strang prepared and delivered lectures; prepared homework problems.

Teaching Assistants 

Three teaching assistants held office hours to help students with homework problems; fielded email questions from students; graded homework problem sets.

Course Info

Learning Resource Types

theaters Lecture Videos
assignment Problem Sets
co_present Instructor Insights