01247    a2200193   4500003000400000005001700004008004100021020001800062100001600080245002200096260003500118300001000153520071500163650005800878650002700936650003100963650002500994650003401019OSt20251210123157.0251209b        |||||||| |||| 00| 0 eng d  a9781259096952  aMitchell T.  aMachine Learning   bMcGrawHill Educ2013aNew York  a414pp  aThis 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, reinforcement learning, and inductive logic programming.

Writing style is clear, explanatory and precise."01aConcept Learning and the General-to-Specific Ordering02aDecision Tree Learning03aArtificial Neural Networks04aEvaluating Hypothese05aComputational Learning Theory