Introduction to Multi-Armed Bandits: Foundations and Trends® in Machine Learning
Autor Aleksandrs Slivkinsen Limba Engleză Paperback – 30 oct 2019
The work on multi-armed bandits can be partitioned into a dozen or so directions. Each chapter tackles one line of work, providing a self-contained introduction and pointers for further reading. Introduction to Multi-Armed Bandits concentrates on fundamental ideas and elementary, teachable proofs over the strongest possible results. It emphasizes accessibility of the material; while exposure to machine learning and probability/statistics would certainly help, a standard undergraduate course on algorithms should suffice for background.
The first four chapters are devoted IID rewards with adversarial rewards being covered in the next 3 chapters. Contextual bandits are discussed in a separate chapter before the monograph concludes with connections to economics. Each chapter contains a section on bibliographic notes and further directions. Many of the chapters conclude with some exercises.
Introduction to Multi-Armed Bandits provides an accessible treatment for students of a topic that has gained importance in the last decade. Lecturers can use it as a text for an introductory course on the subject.
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Specificații
ISBN-13: 9781680836202
ISBN-10: 168083620X
Pagini: 308
Dimensiuni: 156 x 234 x 17 mm
Greutate: 0.44 kg
Editura: Now Publishers Inc
Seria Foundations and Trends® in Machine Learning
ISBN-10: 168083620X
Pagini: 308
Dimensiuni: 156 x 234 x 17 mm
Greutate: 0.44 kg
Editura: Now Publishers Inc
Seria Foundations and Trends® in Machine Learning