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Machine Learning Fundamentals : A Concise Introduction by Hui Jiang (2021, Hardcover)

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Machine Learning Fundamentals : A Concise Introduction, Hardcover by Jiang, Hui, ISBN 1108837042, ISBN-13 9781108837040, Brand New, Free shipping in the US

About this product

Product Identifiers

PublisherCambridge University Press

ISBN-101108837042

ISBN-139781108837040

eBay Product ID (ePID)2321132487

Product Key Features

Number of Pages400 Pages

LanguageEnglish

Publication NameMachine Learning Fundamentals : a Concise Introduction

SubjectGeneral, Computer Vision & Pattern Recognition

Publication Year2021

FeaturesNew Edition

TypeTextbook

Subject AreaComputers, Science

AuthorHui Jiang

FormatHardcover

Dimensions

Item Height1.1 in

Item Length10.2 in

Item Width8.1 in

Additional Product Features

Intended AudienceScholarly & Professional

LCCN2021-038652

Reviews'Dr Jiang has done a superb job in covering many methods, both theoretical and practical, across a broad spectrum of machine learning in this timely book. I worked closely with Dr Jiang on Bayesian speech recognition during late 90's and I have personally witnessed his excellent skills in applying machine learning to solving a wide range of practical problems. In this book, Dr Jiang has expanded his scope into a much wider set of logically organized topics in modern machine learning. The organization of the material is highly unique and cogent. A number of hot topics in machine learning, including deep learning and neural networks, are naturally incorporated in the book, which not only provides sufficient technical depth for the readers but also aligns well with popular toolkits for implementing the related machine learning methods.' Li Deng, formerly of Microsoft Corporation and Citadel LLC

Dewey Edition23

IllustratedYes

Dewey Decimal006.31

Edition DescriptionNew Edition

Table Of Content1. Introduction; 2. Mathematical Foundation; 3. Supervised Machine Learning (in a nutshell); 4. Feature Extraction; 5. Statistical Learning Theory; 6. Linear Models; 7. Learning Discriminative Models in General; 8. Neural Networks; 9. Ensemble Learning; 10. Overview of Generative Models; 11. Unimodal Models; 12. Mixture Models; 13. Entangled Models; 14. Bayesian Learning; 15. Graphical Models.

SynopsisThis lucid, accessible introduction to supervised machine learning presents core concepts in a focused and logical way that is easy for beginners to follow. The author assumes basic calculus, linear algebra, probability and statistics but no prior exposure to machine learning. Coverage includes widely used traditional methods such as SVMs, boosted trees, HMMs, and LDAs, plus popular deep learning methods such as convolution neural nets, attention, transformers, and GANs. Organized in a coherent presentation framework that emphasizes the big picture, the text introduces each method clearly and concisely "from scratch" based on the fundamentals. All methods and algorithms are described by a clean and consistent style, with a minimum of unnecessary detail. Numerous case studies and concrete examples demonstrate how the methods can be applied in a variety of contexts., This lucid and coherent introduction to supervised machine learning presents core concepts in a concise, logical and easy-to-follow way for readers with some mathematical preparation but no prior exposure to machine learning. Coverage includes widely used traditional methods plus recently popular deep learning methods.

LC Classification NumberQ325.5