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
Requirements Satisfied
18.065 can be applied toward a Bachelor of Science in Mathematics, but is not required.
Offered
Every spring semester
The Classroom
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Room 1 of 1 
Lecture
Classes met in a lecture hall with tiered seating for 134, sliding chalkboards, and A/V equipment.
Assessment
Grade Breakdown
The grade for the course is based on homework assignments, labs, and a final project.
Student Information
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
var pieData = [ { value: 25, color: “#931101” }, { value: 75, color: “#eee” } ]; var myPie = new Chart(document.getElementById(“canvas1”).getContext(“2d”)).Pie(pieData); 3 hours per week
Met 3 times per week for 1 hour per session; 39 sessions total.
Out of Class
var pieData = [ { value: 25, color: “#eee” }, { value: 75, color: “#931101” } ]; var myPie = new Chart(document.getElementById(“canvas2”).getContext(“2d”)).Pie(pieData); 9 hours per week
Students were expected to complete weekly problem sets, labs, and a final project.
Semester Breakdown
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No classes throughout MIT
Lecture session
No class session scheduled
Final project due
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.