Machine learning
By: Mitchell, Tom M.
Series: McGraw-Hill Series in Computer Science. Allen B. Tucker.Publisher: New Delhi Tata McGraw Hill 1997Description: xvii,414p. | Binding - Paperback |.ISBN: 978-1-25-909695-2.Subject(s): Computer Engineering
DDC classification: 6.31
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School of Engineering & Technology Reference Section | Reference | 006.31 MIT (Browse shelf) | Not For Loan | E12954 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Checked out to Geeta Desai (ETF018) | 16/01/2024 | E12955 | |
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12956 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12957 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12958 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12959 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12960 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12961 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12962 | ||
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School of Engineering & Technology General Stacks | Circulation | 006.31 MIT (Browse shelf) | Available | E12963 |
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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