Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1107057132
Format: PDF, Kindle
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Introduces machine learning and its algorithmic paradigms, explaining the principles behind automated learning approaches and the considerations underlying their usage.

Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher: Cambridge University Press
ISBN: 1139952749
Format: PDF, Mobi
Download and Read
Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering.

Understanding Machine Learning

Author: Shai Shalev-Shwartz
Publisher:
ISBN: 9781107512825
Format: PDF
Download and Read
"Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability; important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning; and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering"--

Foundations of Machine Learning

Author: Mehryar Mohri
Publisher: MIT Press
ISBN: 0262304732
Format: PDF, ePub, Mobi
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This graduate-level textbook introduces fundamental concepts and methods in machine learning. It describes several important modern algorithms, provides the theoretical underpinnings of these algorithms, and illustrates key aspects for their application. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning fills the need for a general textbook that also offers theoretical details and an emphasis on proofs. Certain topics that are often treated with insufficient attention are discussed in more detail here; for example, entire chapters are devoted to regression, multi-class classification, and ranking. The first three chapters lay the theoretical foundation for what follows, but each remaining chapter is mostly self-contained. The appendix offers a concise probability review, a short introduction to convex optimization, tools for concentration bounds, and several basic properties of matrices and norms used in the book.The book is intended for graduate students and researchers in machine learning, statistics, and related areas; it can be used either as a textbook or as a reference text for a research seminar.

Machine Learning

Author: Peter Flach
Publisher: Cambridge University Press
ISBN: 1107096391
Format: PDF, Docs
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Covering all the main approaches in state-of-the-art machine learning research, this will set a new standard as an introductory textbook.

Machine Learning Refined

Author: Jeremy Watt
Publisher: Cambridge University Press
ISBN: 1316558800
Format: PDF, Mobi
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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.

Machine Learning

Author: Kevin P. Murphy
Publisher: MIT Press
ISBN: 0262018020
Format: PDF, ePub, Mobi
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A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.

A Probabilistic Theory of Pattern Recognition

Author: Luc Devroye
Publisher: Springer Science & Business Media
ISBN: 1461207118
Format: PDF, ePub, Docs
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A self-contained and coherent account of probabilistic techniques, covering: distance measures, kernel rules, nearest neighbour rules, Vapnik-Chervonenkis theory, parametric classification, and feature extraction. Each chapter concludes with problems and exercises to further the readers understanding. Both research workers and graduate students will benefit from this wide-ranging and up-to-date account of a fast- moving field.

Principles and Theory for Data Mining and Machine Learning

Author: Bertrand Clarke
Publisher: Springer Science & Business Media
ISBN: 0387981357
Format: PDF, ePub, Mobi
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Extensive treatment of the most up-to-date topics Provides the theory and concepts behind popular and emerging methods Range of topics drawn from Statistics, Computer Science, and Electrical Engineering