First-Order and Stochastic Optimization Methods for Machine Learning

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.

本書不僅涵蓋了基礎材料，還介紹了過去幾年在機器學習算法領域取得的最新進展。儘管在這個領域進行了大量的研究和開發，但目前還沒有一個系統性的教材來介紹機器學習算法的基本概念和最新進展，特別是那些基於隨機優化方法、隨機算法、非凸優化、分佈式和在線學習以及無投影方法的算法。本書將以教程的方式呈現這些最新發展，從基本構建塊到經過精心設計且複雜的機器學習算法，使機器學習、人工智能和數學編程社區的廣大讀者受益。