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Book Synopsis
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:
  • Thoroughly updat

    • Introduction to Online Convex Optimization Second

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      A Hardback by Elad Hazan

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        Publisher: MIT Press Ltd
        Publication Date: Publication Date: 06/09/2022
        ISBN13: 9780262046985, 978-0262046985
        ISBN10: 0262046989
        Also in:
        Mathematics Optimization

        Description

        Book Synopsis
        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:
      • Thoroughly updat

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