Machine learning refined : foundations, algorithms, and applications / Jeremy Watt, Reza Borhani, and Aggelos K. Katsaggelos.
By: Watt, Jeremy [author.].
Contributor(s): Borhani, Reza | Katsaggelos, Aggelos Konstantinos.
Publisher: New York : Cambridge University Press, 2016Description: xiii, 286 pages : illustrations ; 26 cm.Content type: text Media type: unmediated Carrier type: volumeISBN: 9781107123526.Subject(s): Machine learningGenre/Form: Print books.Summary: "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"--| Current location | Call number | Status | Date due | Barcode | Item holds |
|---|---|---|---|---|---|
| On Shelf | Q325.5 .W38 2016 (Browse shelf) | Available | AU00000000014688 |
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| Q325.5 .S475 2014 Understanding machine learning : from theory to algorithms / | Q325.5 .S5 2018 Introduction to deep learning : from logical calculus to artificial intelligence / | Q325.5 .T73 2020 Fundamentals of machine learning / | Q325.5 .W38 2016 Machine learning refined : foundations, algorithms, and applications / | Q325.5 .Z44 2018 Feature engineering for machine learning : principles and techniques for data scientists / | Q325.6 .G73 2020 Foundations of deep reinforcement learning : theory and practice in Python / | Q325.6 .R45 2018 Reinforcement learning : an introduction / |
"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"--