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Jeffrey A. Fess Linear Algebra for Data Science, Machine (Hardback) (UK IMPORT)
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US $139.15
ApproximatelyS$ 178.13
Condition:
Brand New
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Located in: Rushden, United Kingdom
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eBay item number:365733804976
Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- Book Title
- Linear Algebra for Data Science, Machine Learning, and Signal Pro
- Title
- Linear Algebra for Data Science, Machine Learning, and Signal Pro
- EAN
- 9781009418140
- ISBN
- 9781009418140
- Genre
- Science Nature & Math
- Topic
- Computing & Internet
- Release Date
- 05/16/2024
- Release Year
- 2024
- Country/Region of Manufacture
- GB
- Item Weight
- 920g
About this product
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1009418149
ISBN-13
9781009418140
eBay Product ID (ePID)
2335462753
Product Key Features
Number of Pages
450 Pages
Publication Name
Linear Algebra for Data Science, Machine Learning, and Signal Processing
Language
English
Subject
General, Computer Vision & Pattern Recognition
Publication Year
2024
Type
Textbook
Subject Area
Mathematics, Computers
Format
Hardcover
Dimensions
Item Height
1.2 in
Item Length
9.9 in
Item Width
6.9 in
Additional Product Features
LCCN
2023-050878
Dewey Edition
23
Reviews
'The authors provide a comprehensive contemporary presentation of linear algebra, demonstrating its foundational and intrinsic value to modern subjects, such as machine/deep learning, data science, and signal processing. The presentation is fun, exciting, topic-diverse, classroom tested, and addresses practical implementation in ways that jump start students' use.' Christ D. Richmond, Duke University
Illustrated
Yes
Dewey Decimal
512.5
Table Of Content
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.
Synopsis
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., Master basic matrix methods by seeing how the mathematics 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.
LC Classification Number
QA184.2.F47 2024
Item description from the seller
Seller business information
VAT number: GB 864154811
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- eBay automated feedback- Feedback left by buyer.Past monthOrder completed successfully—tracked and on time
- eBay automated feedback- Feedback left by buyer.Past monthOrder completed successfully—tracked and on time
- eBay automated feedback- Feedback left by buyer.Past monthOrder completed successfully—tracked and on time