Description: Understanding Machine Learning by Shai Shalev-Shwartz, Shai Ben-David Estimated delivery 3-12 business days Format Hardcover Condition Brand New Description Machine learning is one of the fastest growing areas of computer science, with far-reaching applications. This book explains the principles behind the automated learning approach and the considerations underlying its usage. The authors explain the hows and whys of machine-learning algorithms, making the field accessible to both students and practitioners. Publisher Description 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 a theoretical account of the fundamentals underlying machine learning and the mathematical derivations that transform these principles into practical algorithms. Following a presentation of the basics, the book covers a wide array of central topics unaddressed 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 advanced undergraduates or beginning graduates, the text makes the fundamentals and algorithms of machine learning accessible to students and non-expert readers in statistics, computer science, mathematics and engineering. Author Biography Shai Shalev-Shwartz is an Associate Professor at the School of Computer Science and Engineering at the Hebrew University of Jerusalem, Israel. Shai Ben-David is a Professor in the School of Computer Science at the University of Waterloo, Canada. Details ISBN 1107057132 ISBN-13 9781107057135 Title Understanding Machine Learning Author Shai Shalev-Shwartz, Shai Ben-David Format Hardcover Year 2014 Pages 410 Publisher Cambridge University Press GE_Item_ID:79136811; About Us Grand Eagle Retail is the ideal place for all your shopping needs! With fast shipping, low prices, friendly service and over 1,000,000 in stock items - you're bound to find what you want, at a price you'll love! Shipping & Delivery Times Shipping is FREE to any address in USA. Please view eBay estimated delivery times at the top of the listing. Deliveries are made by either USPS or Courier. We are unable to deliver faster than stated. International deliveries will take 1-6 weeks. NOTE: We are unable to offer combined shipping for multiple items purchased. This is because our items are shipped from different locations. Returns If you wish to return an item, please consult our Returns Policy as below: Please contact Customer Services and request "Return Authorisation" before you send your item back to us. Unauthorised returns will not be accepted. Returns must be postmarked within 4 business days of authorisation and must be in resellable condition. Returns are shipped at the customer's risk. We cannot take responsibility for items which are lost or damaged in transit. For purchases where a shipping charge was paid, there will be no refund of the original shipping charge. Additional Questions If you have any questions please feel free to Contact Us. Categories Baby Books Electronics Fashion Games Health & Beauty Home, Garden & Pets Movies Music Sports & Outdoors Toys
Price: 77.77 USD
Location: Fairfield, Ohio
End Time: 2024-11-21T04:04:03.000Z
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ISBN-13: 9781107057135
Book Title: Understanding Machine Learning
Number of Pages: 410 Pages
Language: English
Publication Name: Understanding Machine Learning : from Theory to Algorithms
Publisher: Cambridge University Press
Subject: Algebra / General, Computer Vision & Pattern Recognition
Item Height: 1.1 in
Publication Year: 2014
Item Weight: 32.2 Oz
Type: Textbook
Item Length: 10.2 in
Subject Area: Mathematics, Computers
Author: Shai Ben-David, Shai Shalev-Shwartz
Item Width: 7.2 in
Format: Hardcover