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Machine learning

By: Language: ENG Series: McGraw-Hill Series in Computer Science | Allen B. TuckerAnalytics: Show analyticsPublication details: New Delhi Tata McGraw Hill 1997Description: xvii,414p. | Binding - Paperback |ISBN:
  • 978-1-25-909695-2
Subject(s): DDC classification:
  • 6.31 MIT DDC23
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Item type Current library Collection Call number Status Barcode
Books Books School of Engineering & Technology Reference Section Reference 006.31 MIT (Browse shelf(Opens below)) Not For Loan E12954
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12955
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12956
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12957
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12958
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12959
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12960
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12961
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12962
Books Books School of Engineering & Technology General Stacks Circulation 006.31 MIT (Browse shelf(Opens below)) Available E12963
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006.31 MIT Machine learning 006.31 MIT Machine learning 006.31 MIT Machine learning 006.31 MIT Machine learning 006.31 MIT Machine learning 006.31 MIT Machine learning 006.31 MIT Machine learning

This textbook provides a single source introduction to the primary approaches to machine learning. It is intended for advanced undergraduate and graduate students, as well as for developers and researchers in the field. No prior background in artificial intelligence or statistics is assumed. Several key algorithms, example date sets and project- oriented home work assignments discussed in the book are accessible through the World Wide Web.
Feature:
The book covers the concepts and techniques from the various fields in a unified fashion
Covers very recent subjects such as genetic algorithms, re-enforcement learning and inductive logic programming.
Writing style is clear, explanatory and precise.

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