000			01360 a2200217 4500
003			OSt
005			20251210123157.0
008			251209b |||||||| |||| 00| 0 eng d
020			_a9781259096952
100			_aMitchell T. _9215695
245			_aMachine Learning
260			_bMcGrawHill Edu _c2013 _aNew York
300			_a414pp
520			_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."
650	0	1	_aConcept Learning and the General-to-Specific Ordering _9215761
650	0	2	_aDecision Tree Learning _9215762
650	0	3	_aArtificial Neural Networks _9215763
650	0	4	_aEvaluating Hypothese _9215764
650	0	5	_aComputational Learning Theory _9215765
942			_cBK _2ddc
999			_c369314 _d369314