Introduction to Online Convex Optimization, second edition
by
Elad Hazan
Book Details
Format
Hardback or Cased Book
Book Series
Adaptive Computation and Machine Learning series
ISBN-10
0262046989
ISBN-13
9780262046985
Publisher
MIT Press Ltd
Imprint
MIT Press
Country of Manufacture
GB
Country of Publication
GB
Publication Date
Sep 6th, 2022
Print length
256 Pages
Weight
460 grams
Dimensions
15.80 x 23.70 x 1.80 cms
Product Classification:
Optimization
Ksh 10,250.00
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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:
Thoroughly updated material throughout New chapters on boosting, adaptive regret, and approachability and expanded exposition on optimization Examples of applications, including prediction from expert advice, portfolio selection, matrix completion and recommendation systems, SVM training, offered throughout Exercises that guide students in completing parts of proofs
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:
Get Introduction to Online Convex Optimization, second edition by at the best price and quality guaranteed only at Werezi Africa's largest book ecommerce store. The book was published by MIT Press Ltd and it has pages.
