Linear Algebra in Data Science

This textbook explores applications of linear algebra in data science at an introductory level, showing readers how the two are deeply connected. The authors accomplish this by offering exercises that escalate in complexity, many of which incorporate MATLAB. Practice projects appear as well for students to better understand the real-world applications of the material covered in a standard linear algebra course. Some topics covered include singular value decomposition, convolution, frequency filtering, and neural networks. Linear Algebra in Data Science is suitable as a supplement to a standard linear algebra course.

Explores applications of linear algebra in data science, showing readers how the two are connected Offers exercises that escalate in complexity, many of which incorporate MATLAB Includes practice projects that show real-world applications of the material covered in a standard linear algebra course

Autorentext

Vincente Montesinos is a Corresponding Member of the Spanish Royal Academy of Sciences, and is a Professor at the Universidad Politécnica de Valencia, Spain. Peter Zizler is an Associate Professor at Mount Royal University in Calgary, Canada. Václav Zizler is the former Head of Research at the Mathematical Institute of the Czech Academy of Sciences, and previously served as Faculty Lecturer at the University of Alberta, and Professor of Mathematics at the University of Alberta, Edmonton. In 2001, his book Functional Analysis and Infinite Dimensional Geometry was named university textbook of the year by the Czech Minister of Education.

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