Machine learning refined

Machine learning refined: foundations, algorithms, and applications

by Watt, Jeremy

Type Book
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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 pr…

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Title Statements

  • Full title :Machine learning refined : foundations, algorithms, and applications / Jeremy Watt, Reza Borhani, Aggelos K. Katsaggelos.

Summary

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.

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Edition Statements

  • Publication Date:2020.
  • Edition:Second edition

Publication Statements

Notes

Title from publisher's bibliographic system (viewed on 07 Feb 2020).

The Physical Object

  • Description:1 online resource (xxi, 574 pages) : : PDF file(s).

Edition Identifiers

  • ISBN:9781108690935 (ebook), 1108690939 (ebook)
  • oclc:on1145279511
  • LC Classification:Q325.5
  • Dewey Decimal:006.3/1

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