TY  - BOOK
AU  - Fessler, Jeffrey A
AU  - Nadakuditi, Raj Rao
TI  - Linear algebra for data science, machine learning, and signal processing
SN  - 9781009418140
U1  - 512.5 
PY  - 2024///
CY  - New York
PB  - Cambridge University Press
KW  - Linear algebra
KW  - Data science
KW  - Machine learning
N1  - Table of contents:
1 - Getting Started

pp 1-11
2 - Introduction to Matrices

pp 12-62
3 - Matrix Factorization: Eigendecomposition and SVD

pp 63-95
4 - Subspaces, Rank, and Nearest-Subspace Classification

pp 96-142
5 - Linear Least-Squares Regression and Binary Classification

pp 143-196
6 - Norms and Procrustes Problems

pp 197-237
7 - Low-Rank Approximation and Multidimensional Scaling

pp 238-282
8 - Special Matrices, Markov Chains, and PageRank

pp 283-334
9 - Optimization Basics and Logistic Regression

pp 335-364
10 - Matrix Completion and Recommender Systems

pp 365-380
11 - Neural Network Models

pp 381-389
12 - Random Matrix Theory, Signal + Noise Matrices, and Phase Transitions

pp 390-404

[https://www.cambridge.org/highereducation/books/linear-algebra-for-data-science-machine-learning-and-signal-processing/1D558680AF26ED577DBD9C4B5F1D0FED#contents]
N2  - Maximise 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#contents)
ER  -