Math 210 introduces students to the tools of linear algebra and optimization, including solving linear systems, matrices as linear transformations, eigenvalues and eigenvectors, singular value decomposition, derivatives, and the method of gradient descent. This course includes applications to data science such as image compression, principal component analysis, and neural networks. Computational tools such as Python will be used to implement algorithms, but no programming experience is needed.
This course is intended for data science minors and for students who want to learn linear algebra and optimization techniques for other quantitative fields.
Here is a syllabus and schedule from Spring 2023.
Videos for this course are available on the instructor’s youtube channel, on the Linear Algebra playlist.
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