01490    a2200229   4500003000400000005001700004008004100021020001800062100002400080245002200104260003500126300001000161520071500171650006600886650003500952650003900987650003301026650004201059942001201101999001901113952012801132OSt20251210123157.0251209b        |||||||| |||| 00| 0 eng d  a9781259096952  aMitchell T.9215695  aMachine Learning   bMcGrawHill Educ2013aNew 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."01aConcept Learning and the General-to-Specific Ordering921576102aDecision Tree Learning921576203aArtificial Neural Networks921576304aEvaluating Hypothese921576405aComputational Learning Theory9215765  cBK2ddc  c369314d369314  00102ddc40708MCAa001b001d2025-12-06e8g1155.00l0pKBJP-BK-2888r2025-12-06 00:00:00v1540.00w2025-12-06yBKz2888