Introduction to Online Convex Optimization, second edition
“Introduction to Online Convex Optimization, second edition” by Elad Hazan is a graduate-level textbook published by MIT Press on September 6, 2022. This edition focuses on online convex optimization, presenting a machine learning framework that treats optimization as a dynamic process. It addresses the complexities of real-world applications where comprehensive theoretical models may not be feasible, integrating elements of mathematical optimization, game theory, and learning theory.
Readers will find updated material throughout, including new chapters on boosting, adaptive regret, and approachability, along with expanded discussions on optimization. The book includes practical examples such as prediction from expert advice, portfolio selection, and recommendation systems, making it relevant for those interested in data science and machine learning. With 248 pages, this edition aims to guide students through exercises that enhance their understanding of the subject matter.
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New edition of a graduate-level textbook on that focuses on online convex optimization, a machine learning framework that views optimization as a process.
In many practical applications, the environment is so complex that it is not feasible to lay out a comprehensive theoretical model and use classical algorithmic theory and/or mathematical optimization. Introduction to Online Convex Optimization presents a robust machine learning approach that contains elements of mathematical optimization, game theory, and learning theory: an optimization method that learns from experience as more aspects of the problem are observed. This view of optimization as a process has led to some spectacular successes in modeling and systems that have become part of our daily lives.
Based on the “Theoretical Machine Learning” course taught by the author at Princeton University, the second edition of this widely used graduate level text features:
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