Learn concepts in linear algebra and matrix analysis, and implement them in MATLAB and Python.
InstructorMike X CohenInstruction prioritizes geometric intuition and visual explanations of concepts like diagonalization and SVD over manual arithmetic.
Editorial course preview
These 3 complementary views highlight concrete, legible examples from the course presentation.
This image displays the detailed course content list, confirming coverage of essential linear algebra topics including vectors, matrix operations, eigendecomposition, and singular value decomposition.
This slide illustrates the connection between abstract linear algebra concepts and practical implementation by displaying a 3D surface plot alongside the normal equation formula and its corresponding Python code syntax.
This slide illustrates the historical roots of linear algebra by displaying an ancient clay tablet alongside a medieval manuscript containing mathematical text and geometric drawings.
The curriculum uses frequent code challenges to implement mathematical operations such as dot products and the Gram-Schmidt algorithm.
The course assumes familiarity with high school algebra/geometry and provides no introductory instruction for using MATLAB or JupyterLab. Note that a review from October 2025 predates the displayed June 2026 update; while the update label suggests recent maintenance, it does not prove these specific gaps were addressed.
One signal suggests a need for more formal, rigorous on-screen definitions when introducing new concepts. This review also predates the displayed update; the update label does not guarantee that definitional depth has been increased.
The course assumes specific mathematical and software tool proficiency that is not explicitly introduced in the curriculum.
