Suggested preparation: Linear algebra and multivariable calculus.
Optional preparation: Advanced Differentiation: AP Calculus AB: Free Course.
About this course
Develop matrix-calculus techniques used in optimization and machine learning. The lectures examine vectorization, gradients, inner products, automatic differentiation and variations of functionals.
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Professional — WikiFree study-level assessment |
| Language | English |
| Available lessons | 17 |
| Video duration | 13h 55m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Linear algebra and multivariable calculus.
Follow the available videos in sequence, pausing to summarize the main argument or reconstruct a worked example. Keep a short list of assumptions, unfamiliar terms and questions to revisit. Additional reading, problem solving and practice will take time beyond the listed video duration.
Using this video classroom
Select a lesson from the list, or search for a topic. Previous and Next move through the sequence. You can mark lessons complete; progress is stored in this browser without a WikiFree account.
Free access and course scope
The public MIT OpenCourseWare videos in this classroom are free to watch. This listing provides the available playlist recordings, which may include selected lectures, recitations, demonstrations or supporting discussions rather than every component of the original campus course. Textbooks, equipment, software licenses, supervised labs, graded assignments and instructor feedback are not supplied by WikiFree. No purchase is required to watch the videos or use the note-based study approach above.
WikiFree independently organizes these resources for self-paced study. Watching does not award an MIT certificate, academic credit or admission to MIT.
Video provider and credit: MIT OpenCourseWare on YouTube. WikiFree titles, descriptions and cover artwork are independently prepared.
Related courses for your next step
- Review a lower-level optionAdvanced Differentiation: AP Calculus AB: Free Course
Focus: Chain rule · Implicit differentiation
Intermediate - Review a lower-level optionAdvanced Derivatives: AP Calculus BC: Free Course
Focus: Implicit differentiation · Inverse functions
Intermediate - Review a lower-level optionFree MIT Course: Matrix Methods for Data Science and Machine Learning
Focus: Course Introduction of 18.065 by Professor Strang · 9. Four Ways to Solve Least Squares Problems
Intermediate
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — Free MIT Course: Matrix Calculus for Machine Learning | Professional | MIT Course: Matrix Calculus for Machine Learning | 13h 55m of video; practice time is additional |
| Advanced Differentiation: AP Calculus AB: Free Course | Intermediate | Chain rule · Implicit differentiation · Inverse derivatives | 3h 44m of video; practice time is additional |
| Advanced Derivatives: AP Calculus BC: Free Course | Intermediate | Implicit differentiation · Inverse functions · Parametric derivatives | 3h 56m of video; practice time is additional |
| Free MIT Course: Matrix Methods for Data Science and Machine Learning | Intermediate | Course Introduction of 18.065 by Professor Strang · 9. Four Ways to Solve Least Squares Problems · Lecture 18: Counting Parameters in SVD, LU, QR, Saddle Points | 28h 9m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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