First-order and stochastic optimization methods for machine learning

First-order and stochastic optimization methods for machine learning / Guanghui Lan.

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 co...

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Bibliographic Details
Main Author: Lan, Guanghui, 1976- (Author)
Format: eBook
Language:English
Published: Cham : Springer, 2020.
Series:Springer series in the data sciences.
Subjects:
Mathematical optimization.
Machine learning.
Optimization.
Computers > Intelligence (AI) & Semantics.
Mathematics > Applied.
Matemáticas > Estadística Aplicada
Aprendizaje automático (Inteligencia artificial)
Machine learning
Mathematical optimization
Electronic books.
Online Access:Click for online access
Table of Contents:
  • Machine 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.

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