<?xml version="1.0" encoding="UTF-8"?>
<mods xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://www.loc.gov/mods/v3" version="3.1" xsi:schemaLocation="http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-1.xsd">
  <titleInfo>
    <title>Understanding Machine Learning:From theory to algorithms</title>
  </titleInfo>
  <name type="personal">
    <namePart>Shalev-Shwartz,Shai</namePart>
    <role>
      <roleTerm authority="marcrelator" type="text">creator</roleTerm>
    </role>
  </name>
  <name type="personal">
    <namePart>Ben-David,Shai</namePart>
  </name>
  <typeOfResource>text</typeOfResource>
  <originInfo>
    <place>
      <placeTerm type="text">New Delhi</placeTerm>
    </place>
    <publisher>Cambridge University Press</publisher>
    <dateIssued>2020</dateIssued>
    <issuance>monographic</issuance>
  </originInfo>
  <language>
    <languageTerm authority="iso639-2b" type="code">eng</languageTerm>
  </language>
  <physicalDescription>
    <form authority="marcform">print</form>
    <extent>397</extent>
  </physicalDescription>
  <note>DescriptionContentsResourcesCoursesAbout the Authors
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.

This book is written for the first year students of Engineering– A blend of theory and solved problems will equip the students with the fundamental knowledge and application of the coding concepts. This will nurture them to have a strong foundation for the courses in the subsequent semesters.

This book ensures a smooth and successful transition to being a skilled Python expert. The book uses a simple-to-complex and easy-to-learn approach throughout. The concept of ‘learning by-solving has been stressed everywhere in the book. Each feature of Python is treated in depth followed by a complete program example to illustrate its use. Wherever necessary, concepts are explained pictorially to facilitate better understanding. The book presents a contemporary approach to programming, offering a combination of theory and practice.</note>
  <subject>
    <topic>Machine Learning</topic>
  </subject>
  <classification authority="ddc">006.31 SHA</classification>
  <identifier type="isbn">978-1-107-51282-5</identifier>
  <identifier type="">Allied Informatics, Jaipur</identifier>
  <recordInfo>
    <recordContentSource authority="marcorg">BSDU</recordContentSource>
    <recordCreationDate encoding="marc">200118</recordCreationDate>
    <recordChangeDate encoding="iso8601">20200224142453.0</recordChangeDate>
    <languageOfCataloging>
      <languageTerm authority="iso639-2b" type="code">English</languageTerm>
    </languageOfCataloging>
  </recordInfo>
</mods>
