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Machine Learning Refined : Foundations, Algorithms, and Applications by Reza Borhani, Jeremy Watt and Aggelos Katsaggelos (2020, Hardcover)
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About this product
Product Identifiers
PublisherCambridge University Press
ISBN-101108480721
ISBN-139781108480727
eBay Product ID (ePID)21038911704
Product Key Features
Number of Pages594 Pages
LanguageEnglish
Publication NameMachine Learning Refined : Foundations, Algorithms, and Applications
SubjectSignals & Signal Processing
Publication Year2020
FeaturesRevised
TypeTextbook
AuthorReza Borhani, Jeremy Watt, Aggelos Katsaggelos
Subject AreaTechnology & Engineering
FormatHardcover
Dimensions
Item Height1.1 in
Item Weight48 Oz
Item Length10 in
Item Width7.2 in
Additional Product Features
Edition Number2
Intended AudienceScholarly & Professional
Dewey Edition23
Reviews'An excellent book that treats the fundamentals of machine learning from basic principles to practical implementation. The book is suitable as a text for senior-level and first-year graduate courses in engineering and computer science. It is well organized and covers basic concepts and algorithms in mathematical optimization methods, linear learning, and nonlinear learning techniques. The book is nicely illustrated in multiple colors and contains numerous examples and coding exercises using Python.' John G. Proakis, University of California, San Diego
IllustratedYes
Dewey Decimal006.3/1
Table Of Content1. Introduction to machine learning; Part I. Mathematical Optimization: 2. Zero order optimization techniques; 3. First order methods; 4. Second order optimization techniques; Part II. Linear Learning: 5. Linear regression; 6. Linear two-class classification; 7. Linear multi-class classification; 8. Linear unsupervised learning; 9. Feature engineering and selection; Part III. Nonlinear Learning: 10. Principles of nonlinear feature engineering; 11. Principles of feature learning; 12. Kernel methods; 13. Fully-connected neural networks; 14. Tree-based learners; Part IV. Appendices: Appendix A. Advanced first and second order optimization methods; Appendix B. Derivatives and automatic differentiation; Appendix C. Linear algebra.
Edition DescriptionRevised edition
SynopsisAn intuitive approach to machine learning detailing the key concepts needed to build products and conduct research. Featuring color illustrations, real-world examples, practical coding exercises, and an online package including sample code, data sets, lecture slides, and solutions. It is ideal for graduate courses, reference, and self-study., With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.
LC Classification NumberQ325.5.W38 2020
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