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.
Matrix Methodsミネソタ大学（University of Minnesota）
ミネソタ大学（University of Minnesota）
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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MATRIX METHODS からの人気レビュー
This Course content is very good and has good real-time examples. However, the Instructor should add a few videos on SVD, Maximum dilation, and Shrinkage and Direction of Maximum Dilation.
Pros and cons. Sometimes it's hard to find in this course needed information to solve Assignments.\n\nBut you have to dig deeper from outside sources.
Thank you so much for giving me this opportunity to learn about matrix methods. This is helpful for my career and it is useful to all the beginners.
Its a very good experience for me and it helps me to learn new topics and known new matters. Thank You Coursera.