Suggested preparation: Linear algebra, calculus and introductory programming.
About this course
Study matrix techniques that support data science and machine learning. The lectures connect norms, singular values, low-rank structure and neural networks with practical mathematical reasoning.
Topics and lesson examples
- 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
- 26. Structure of Neural Nets for Deep Learning
- 35. Finding Clusters in Graphs
Course at a glance
| Provider | MIT |
|---|---|
| Platform | YouTube · MIT OpenCourseWare |
| Access | Free Video |
| Level | Intermediate — WikiFree study-level assessment |
| Language | English |
| Available lessons | 36 |
| Video duration | 28h 9m · self-paced |
| Details checked | 10 October 2026 |
Preparation and study approach
Suggested preparation: Linear algebra, calculus and introductory programming.
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.
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Intermediate
Compare course focus, level and study time
| Course | Level | Focus | Study time |
|---|---|---|---|
| This course — 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 |
| Free MIT Course: Matrix Calculus for Machine Learning | Professional | MIT Course: Matrix Calculus for Machine Learning | 13h 55m of video; practice time is additional |
| Free MIT Course: Computational Science and Engineering Methods | Professional | Recitation 2: Boundary Values, Ramp Functions and Delta Functions · Recitation 5: Mass-Spring Systems and Energy-Conserving Methods · Recitation 8: Finite Elements and Boundary Conditions | 42h 29m of video; practice time is additional |
| Free MIT Course: Linear Algebra: Matrices, Eigenvalues and Vector Spaces | Intermediate | An Interview with Gilbert Strang on Teaching Linear Algebra · 8. Solving Ax = b: Row Reduced Form R · 17. Orthogonal Matrices and Gram-Schmidt | 35h 49m of video; practice time is additional |
Provider: MIT. Videos play from the MIT OpenCourseWare channel on YouTube.
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