9781107123526-1107123526-Machine Learning Refined: Foundations, Algorithms, and Applications

Machine Learning Refined: Foundations, Algorithms, and Applications

ISBN-13: 9781107123526
ISBN-10: 1107123526
Edition: 1
Author: Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos
Publication date: 2016
Publisher: Cambridge University Press
Format: Hardcover 298 pages
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Book details

ISBN-13: 9781107123526
ISBN-10: 1107123526
Edition: 1
Author: Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos
Publication date: 2016
Publisher: Cambridge University Press
Format: Hardcover 298 pages

Summary

Machine Learning Refined: Foundations, Algorithms, and Applications (ISBN-13: 9781107123526 and ISBN-10: 1107123526), written by authors Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos, was published by Cambridge University Press in 2016. With an overall rating of 3.9 stars, it's a notable title among other AI & Machine Learning (Computer Science) books. You can easily purchase or rent Machine Learning Refined: Foundations, Algorithms, and Applications (Hardcover) from BooksRun, along with many other new and used AI & Machine Learning books and textbooks. And, if you're looking to sell your copy, our current buyback offer is $0.3.

Description

Providing a unique approach to machine learning, this text contains fresh and intuitive, yet rigorous, descriptions of all fundamental concepts necessary to conduct research, build products, tinker, and play. By prioritizing geometric intuition, algorithmic thinking, and practical real world applications in disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology, this text provides readers with both a lucid understanding of foundational material as well as the practical tools needed to solve real-world problems. With in-depth Python and MATLAB/OCTAVE-based computational exercises and a complete treatment of cutting edge numerical optimization techniques, this is an essential resource for students and an ideal reference for researchers and practitioners working in machine learning, computer science, electrical engineering, signal processing, and numerical optimization. Additional resources including supplemental discussion topics, code demonstrations, and exercises can be found on the official textbook website at mlrefined.com

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