Machine learning refined: foundations, algorithms, and applications

By:

Watt, Jeremy

Contributor(s):

Publication details: Cambridge University Press, 2016. New York:Description: 286 p.; ill.; 25 cmISBN:

9781107123526

Subject(s):

Machine learning

DDC classification:

006.31 WAT

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

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Item type	Current library	Call number	Status	Date due	Barcode
Books	IIT Gandhinagar	006.31 WAT (Browse shelf(Opens below))	Available		024048

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Previous	006.31 SUT Reinforcement learning: an introduction	006.31 SUT Reinforcement learning: an introduction	006.31 THE Machine learning: a Bayesian and optimization perspective	006.31 WAT Machine learning refined:	006.31 WAT Machine learning refined: foundations, algorithms, and applications	006.31 WAT Algebraic geometry and statistical learning theory	006.31 WIT Quantum machine learning: what quantum computing means to data mining	Next

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

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