Foundations of Machine Learning (Adaptive Computation and Machine Learning series) - Image 1

£53.03

MIT Press Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

Name: Foundations of Machine Learning (Adaptive Computation and Machine Learning series)
Brand: MIT Press
Price: 53.03 GBP
Availability: InStock

White

AI & Machine Learning

Price History & Forecast

Last 1 days • 1 data points (No recent data available)

Historical

Generating forecast...

Not enough data points to display chart (need at least 2 points)

Price Distribution

Price distribution over 1 days • 1 price levels

Days at Price

Price Analysis

Most common price: £53 (1 days, 100.0%)

Price range: £53 - £53

Price levels: 1 different prices over 1 days

Description

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms. This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in machine learning while providing the theoretical basis and conceptual tools needed for the discussion and justification of algorithms. It also describes several key aspects of the application of these algorithms. The authors aim to present novel theoretical tools and concepts while giving concise proofs even for relatively advanced topics. Foundations of Machine Learning is unique in its focus on the analysis and theory of algorithms. The first four chapters lay the theoretical foundation for what follows; subsequent chapters are mostly self-contained. Topics covered include the Probably Approximately Correct (PAC) learning framework; generalization bounds based on Rademacher complexity and VC-dimension; Support Vector Machines (SVMs); kernel methods; boosting; on-line learning; multi-class classification; ranking; regression; algorithmic stability; dimensionality reduction; learning automata and languages; and reinforcement learning. Each chapter ends with a set of exercises. Appendixes provide additional material including concise probability review. This second edition offers three new chapters, on model selection, maximum entropy models, and conditional entropy models. New material in the appendixes includes a major section on Fenchel duality, expanded coverage of concentration inequalities, and an entirely new entry on information theory. More than half of the exercises are new to this edition.

Key Features

New Store Stock

Product Specifications

Brand: MIT Press
Colour: White
Format: hardcover
ASIN: 0262039400
Category: Books > Subjects > Computing & Internet > Computer Science > AI & Machine Learning
Domain: Amazon UK
Release Date: 02 April 2019
Listed Since: 18 September 2018

Barcode

No barcode data available

MIT Press Foundations of Machine Learning (Adaptive Computation and Machine Learning series)

Price History & Forecast

Price Distribution

Price Analysis

Description

Key Features

Product Specifications

Barcode

Similar Products You Might Like

Foundations of Computer Vision (Adaptive Computation and Machine Learning)

Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies

Machine Learning: A Constraint-Based Approach

Machine Learning: An Algorithmic Perspective, Second Edition

Learning Theory from First Principles (Adaptive Computation and Machine Learning)

Machine Learning – A Probabilistic Perspective

Machine Learning Algorithms: Popular algorithms for data science and machine learning, 2nd Edition

Introduction to Online Convex Optimization, Second Edition (Adaptive Computation and Machine Learning) (Adaptive Computation and Machine Learning series)

Foundations of Deep Learning (Machine Learning: Foundations, Methodologies, and Applications)

Machine Learning: Theory and Practice

Fundamentals of Pattern Recognition and Machine Learning

Machine Learning: A Practical Approach on the Statistical Learning Theory

Machine Learning: A Bayesian and Optimization Perspective

Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond (Adaptive Computation and Machine Learning series)

Learning Kernel Classifiers: Theory and Algorithms (Adaptive Computation and Machine Learning series)

Machine Learning

Machine Learning

Understanding Machine Learning: From Theory to Algorithms

Fairness and Machine Learning: Limitations and Opportunities (Adaptive Computation and Machine Learning)

Optimization for Machine Learning (Neural Information Processing series)

Neural Networks and Deep Learning: A Textbook

Probability for Statistics and Machine Learning: Fundamentals and Advanced Topics (Springer Texts in Statistics)

Fundamentals and Methods of Machine and Deep Learning: Algorithms, Tools, and Applications

Machine Learning Foundations: Volume 1: Supervised Learning