Mathematical Matrix Methods lie at the root of most methods of machine learning and data analysis of tabular data. Learn the basics of Matrix Methods, including matrix-matrix multiplication, solving linear equations, orthogonality, and best least squares approximation. Discover the Singular Value Decomposition that plays a fundamental role in dimensionality reduction, Principal Component Analysis, and noise reduction. Optional examples using Python are used to illustrate the concepts and allow the learner to experiment with the algorithms.
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The University of Minnesota is among the largest public research universities in the country, offering undergraduate, graduate, and professional students a multitude of opportunities for study and research. Located at the heart of one of the nation’s most vibrant, diverse metropolitan communities, students on the campuses in Minneapolis and St. Paul benefit from extensive partnerships with world-renowned health centers, international corporations, government agencies, and arts, nonprofit, and public service organizations.
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- 1 star8.09%
MATRIX METHODS의 최상위 리뷰
Course is well designed and gives application knowledge of matrices and decomlosition of matrix by SVD method.
It was a great opportunity to know more abut matrices and their characteristics
Succinct, informative, efficient. Thank you, Dr. Boley.
I have learn many ways to solve a lot of problems in Algebra, in easy mode. This Course is usepful and give some tools for your Mathlife.
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