Opening book details…
Can I read Linear Algebra for Data Science, Machine Learning, and Signal Processing on EtoBox?
Linear Algebra for Data Science, Machine Learning, and Signal Processing by Jeffrey A. Fessler, Raj Rao Nadakuditi is a computer science book available to read on EtoBox.
What is Linear Algebra for Data Science, Machine Learning, and Signal Processing about?
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 proble
Who reads Linear Algebra for Data Science, Machine Learning, and Signal Processing?
It is typically read by working professionals who need an authoritative practice reference.
Common subject areas: medicine, law, business, engineering.
- Author
- Jeffrey A. Fessler, Raj Rao Nadakuditi
- Publisher
- Cambridge University Press [CUP]
- Published
- 2024
- Language
- EN
- ISBN
- 9781009418140
- Category
- computer science
- Subjects
- Mathematics, Optical Data Processing, Linear
- Updated
- 2026-03-24
More by Jeffrey A. Fessler, Raj Rao Nadakuditi
Browse all works by Jeffrey A. Fessler, Raj Rao Nadakuditi
Similar books
- Linear Algebra, Data Science, and Machine Learning — Peter J. Olver Jeff Calder (2025)
- Linear Algebra with Machine Learning and Data — Crista Arangala (2023)
- Practical Linear Algebra for Data Science — Mike X. Cohen
- Machine Learning for Signal Processing : Data Science, Algorithms, and Computational Statistics — Max A. Little (2019)
- Linear Algebra for Computer Vision, Robotics, and Machine Learning — Quaintance J. Gallier J. (2019)
- Signal Processing and Machine Learning for Biomedical Big Data — Falk, Tiago H.; Sejdic, Ervin (2018)