A university-level linear algebra course covering vector spaces, matrices, linear systems, eigenvalues, and related topics.
Professor Gilbert Strang's legendary MIT 18.06 Scholar course is the gold-standard university curriculum for applied linear algebra. Divided into three units—Ax = b and the Four Subspaces, Least Squares and Orthogonality, and Eigenvalues and Positive Definite Matrices (including SVD)—it provides complete lecture videos, recitation problem walkthroughs, and exams with solutions.
Course status is tracked independently from roadmap topic checkboxes so you can mark external courses without altering your topic mastery percentage.
Phase 1 Core University Course (Module 1.1 Linear Algebra)
Complete MIT OpenCourseWare Scholar edition with full video lectures, recitation videos, problem sets, and exams with solutions—100% free.
Status: Verified (2026-11-09)
Review these foundations before starting so you get the most out of this material:
In Phase 1: Mathematics for AI
Pair directly with 3Blue1Brown during Module 1.1. Focus on Matrix Multiplication, Orthogonality/Projections, Eigenvalues, and Singular Value Decomposition (SVD).
Visual explanations of vectors, transformations, matrix multiplication, eigenvectors, and the intuition behind linear algebra.
Mathematical foundations of machine learning, including linear algebra, vector calculus, probability, and optimization.
University course on vectors, matrices, partial derivatives, gradient vectors, directional derivatives, and multivariable optimization.