Overview
- Presents comprehensive study of topics in machine learning from introductory material through most complicated algorithms
- Summarizes most recent findings in the area of machine learning
- Addresses a broad audience in machine learning, artificial intelligence, and mathematical programming
- Includes exercises
Part of the book series: Springer Series in the Data Sciences (SSDS)
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Table of contents (8 chapters)
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Front Matter
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Back Matter
Keywords
About this book
This 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.
Authors and Affiliations
Bibliographic Information
Book Title: First-order and Stochastic Optimization Methods for Machine Learning
Authors: Guanghui Lan
Series Title: Springer Series in the Data Sciences
DOI: https://doi.org/10.1007/978-3-030-39568-1
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: Springer Nature Switzerland AG 2020
Hardcover ISBN: 978-3-030-39567-4Published: 16 May 2020
Softcover ISBN: 978-3-030-39570-4Published: 16 May 2021
eBook ISBN: 978-3-030-39568-1Published: 15 May 2020
Series ISSN: 2365-5674
Series E-ISSN: 2365-5682
Edition Number: 1
Number of Pages: XIII, 582
Number of Illustrations: 2 b/w illustrations, 16 illustrations in colour
Topics: Optimization, Machine Learning