Linear Algebra for Data Science, Machine L...

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Sprache: Englisch

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Beschreibung

"Master basic matrix methods by seeing how the math is used in practice in a range of data-driven applications. Includes a wealth of engaging exercises for quizzes, self-study and interactive learning, as well as online JULIA demos offering a hands-on learning experience for upper-level undergraduates and first-year graduate students"--

Über den Autor

Jeffrey A. Fessler is the William L. Root Professor of EECS at the University of Michigan. He received the Edward Hoffman Medical Imaging Scientist Award in 2013, and an IEEE EMBS Technical Achievement Award in 2016. He received the 2023 Steven S. Attwood Award, the highest honor awarded to a faculty member by the College of Engineering at the University of Michigan. He is a fellow of the IEEE and of the AIMBE.

Inhaltsverzeichnis

1. Getting started; 2. Introduction to Matrices; 3. Matrix factorization: eigendecomposition and SVD; 4. Subspaces, rank and nearest-subspace classification; 5. Linear least-squares regression and binary classification; 6. Norms and Procrustes problems; 7. Low-rank approximation and multidimensional scaling; 8. Special matrices, Markov chains and PageRank; 9. Optimization basics and logistic regression; 10. Matrix completion and recommender systems; 11. Neural network models; 12. Random matrix theory, signal+ noise matrices, and phase transitions.

Erscheinungsjahr:	2024
Genre:	Importe , Informatik
Rubrik:	Naturwissenschaften & Technik
Medium:	Buch
ISBN-13:	9781009418140
ISBN-10:	1009418149
Sprache:	Englisch
Einband:	Gebunden
Autor:	Fessler, Jeffrey A. Nadakuditi, Raj Rao
Hersteller:	Cambridge University Pr.
Verantwortliche Person für die EU:	Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße:	246 x 175 x 29 mm
Von/Mit:	Jeffrey A. Fessler (u. a.)
Erscheinungsdatum:	16.05.2024
Gewicht:	0,934 kg