MIT Linear Algebra — Gilbert Strang

Gilbert Strang's linear algebra course connects matrix calculations to the spaces and transformations they describe. That connection is the reason to study it carefully: a correct multiplication is more useful when you understand what changed geometrically. The OpenCourseWare offering includes lectures and problem material for sustained independent study.

What you will learn

• Solve systems of linear equations
• Understand vector spaces and independence
• Interpret matrix transformations
• Analyze eigenvalues and related matrix properties

Who this suits

Useful for an engineering, computing, or mathematics learner with solid algebra. It is particularly relevant before mathematical machine learning, where matrix notation appears constantly. You do not need to rush into a numerical library; small hand-worked examples can expose ideas that large computations hide.

Before you enroll

The course is an archived university offering. Its foundational subject is durable, but independent study still needs a feedback method and regular problem solving. Computational familiarity with arrays is different from understanding vector spaces and the conditions under which a system has a solution.

Free MIT OpenCourseWare lectures and supporting materials. OCW study does not provide enrollment, a completion certificate, or academic credit.

Put it into practice

Construct a small matrix with dependent columns and explain its null space both numerically and as a statement about the transformation.

Source and review

This overview is based on the provider's published information, checked on 11 October 2026. It is an editorial assessment, not a claim that we completed the resource. Time filters are approximate study ranges; projects and exercises can take longer.

View the curriculum and current access options at MIT OpenCourseWare.

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