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Springer Series in the Data Sciences Ser.: First-Order and Stochastic Optimization Methods for Machine Learning by Guanghui Lan (2020, Hardcover)

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About this product

Product Identifiers

PublisherSpringer International Publishing A&G

ISBN-103030395677

ISBN-139783030395674

eBay Product ID (ePID)15038857531

Product Key Features

Number of PagesXiii, 582 Pages

Publication NameFirst-Order and Stochastic Optimization Methods for Machine Learning

LanguageEnglish

SubjectProbability & Statistics / General, Intelligence (Ai) & Semantics, Optimization

Publication Year2020

TypeTextbook

AuthorGuanghui Lan

Subject AreaMathematics, Computers

SeriesSpringer Series in the Data Sciences Ser.

FormatHardcover

Dimensions

Item Weight37.1 Oz

Item Length9.3 in

Item Width6.1 in

Additional Product Features

Number of Volumes1 vol.

IllustratedYes

Table Of ContentMachine Learning Models.- Convex Optimization Theory.- Deterministic Convex Optimization.- Stochastic Convex Optimization.- Convex Finite-sum and Distributed Optimization.- Nonconvex Optimization.- Projection-free Methods.- Operator Sliding and Decentralized Optimization.

SynopsisThis book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

LC Classification NumberQA402.5-402.6