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Machine Learning Refined: Foundations, Algorithms, and Applications
US $48.49
ApproximatelyS$ 62.31
Condition:
Very Good
A book that has been read but is in excellent condition. No obvious damage to the cover, with the dust jacket included for hard covers. No missing or damaged pages, no creases or tears, and no underlining/highlighting of text or writing in the margins. May be very minimal identifying marks on the inside cover. Very minimal wear and tear.
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Located in: Somerset, New Jersey, United States
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Estimated between Sat, 23 Aug and Fri, 29 Aug to 94104
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30 days return. Buyer pays for return shipping. If you use an eBay shipping label, it will be deducted from your refund amount.
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eBay item number:167437423483
Item specifics
- Condition
- ISBN
- 9781108480727
About this product
Product Identifiers
Publisher
Cambridge University Press
ISBN-10
1108480721
ISBN-13
9781108480727
eBay Product ID (ePID)
21038911704
Product Key Features
Number of Pages
594 Pages
Publication Name
Machine Learning Refined : Foundations, Algorithms, and Applications
Language
English
Publication Year
2020
Subject
Signals & Signal Processing
Features
Revised
Type
Textbook
Subject Area
Technology & Engineering
Format
Hardcover
Dimensions
Item Height
1.1 in
Item Weight
48 Oz
Item Length
10 in
Item Width
7.2 in
Additional Product Features
Edition Number
2
Intended Audience
Scholarly & Professional
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
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.3/1
Edition Description
Revised edition
Table Of Content
1. 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.
Synopsis
An 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 Number
Q325.5.W38 2020
Item description from the seller
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