Understanding machine learning : from theory to algorithms

 0 Người đánh giá. Xếp hạng trung bình 0

Tác giả: Shai Ben-David, Shai Shalev-Shwartz

Ngôn ngữ: eng

ISBN-10: 1107057132

ISBN-13: 978-1107057135

Ký hiệu phân loại: 006.31 Machine learning

Thông tin xuất bản: New York, NY, USA : Cambridge University Press, 2014

Mô tả vật lý: xvi, 397 pages : , illustrations ; , 26 cm

Bộ sưu tập: Công nghệ thông tin

ID: 136662

 "Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. The aim of this textbook is to introduce machine learning, and the algorithmic paradigms it offers, in a principled way. The book provides an extensive theoretical account of the fundamental ideas underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics of the field, the book covers a wide array of central topics that have not been addressed by previous textbooks. These include a discussion of the computational complexity of learning and the concepts of convexity and stability
  important algorithmic paradigms including stochastic gradient descent, neural networks, and structured output learning
  and emerging theoretical concepts such as the PAC-Bayes approach and compression-based bounds. Designed for an advanced undergraduate or beginning graduate course, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics, and engineering"-- Provided by publisher.
Includes bibliographical references (pages 385-393) and index.
Tạo bộ sưu tập với mã QR

THƯ VIỆN - TRƯỜNG ĐẠI HỌC CÔNG NGHỆ TP.HCM

ĐT: (028) 71010608 | Email: tt.thuvien@hutech.edu.vn

Copyright @2024 THƯ VIỆN HUTECH