000			02076 a2200241 4500
008			250209b |||||||| |||| 00| 0 eng d
020			_a9781009418140
082			_a512.5 FES
100			_aFessler, Jeffrey A.
245			_aLinear algebra for data science, machine learning, and signal processing
260			_aCambridge: _bCambridge University Press, _c2024.
300			_axix, 431p.: _bhbk.: _c25 cm.
504			_aIncludes Bibliographical References and Index
520			_aMaximise student engagement and understanding of matrix methods in data-driven applications with this modern teaching package. Students are introduced to matrices in two preliminary chapters, before progressing to advanced topics such as the nuclear norm, proximal operators and convex optimization. Highlighted applications include low-rank approximation, matrix completion, subspace learning, logistic regression for binary classification, robust PCA, dimensionality reduction and Procrustes problems. Extensively classroom-tested, the book includes over 200 multiple-choice questions suitable for in-class interactive learning or quizzes, as well as homework exercises (with solutions available for instructors). It encourages active learning with engaging 'explore' questions, with answers at the back of each chapter, and Julia code examples to demonstrate how the mathematics is actually used in practice. A suite of computational notebooks offers a hands-on learning experience for students. This is a perfect textbook for upper-level undergraduates and first-year graduate students who have taken a prior course in linear algebra basics. https://www.cambridge.org/highereducation/books/linear-algebra-for-data-science-machine-learning-and-signal-processing/1D558680AF26ED577DBD9C4B5F1D0FED#overview
650			_aMathematics ,
650			_aAlgebra -- Linear & Multilinear
650			_aMultidimensional Algebra
650			_aSignal Processing
650			_aMachine Learning
650			_aData Science
700			_a Nadakuditi, Raj Rao _eCo-author
942			_cTD _2ddc
999			_c61597 _d61597