INTRODUCTION MACHINE LEARNING

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INTRODUCTIONTOMACHINE LEARNINGAN EARLY DRAFT OF A PROPOSEDTEXTBOOKNils J. NilssonRobotics LaboratoryDepartment of Computer ScienceStanford UniversityStanford, CA 94305e-mail: 3, 1998Copyrightc 2005 Nils J. NilssonThis material may not be copied, reproduced, or distributed without thewritten permission of the copyright INTRODUCTION . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . is MACHINE LEARNING ? . . . . . . . . . . . . . . . . . of MACHINE LEARNING . . . . . . . . . . . . . . of MACHINE LEARNING . . . . . . . . . . . . . . . . LEARNING Input-Output Functions . . . . . . . . . . . . . . . . . . of LEARNING . . . . . . . . . . . . . . . . . . . . . . Vectors.

and psychologists study learning in animals and humans. In this book we fo-cus on learning in machines. There are several parallels between animal and machine learning. Certainly, many techniques in machine learning derive from the e orts of psychologists to make more precise their theories of animal and human learning through computational models.

  Machine, Animal, Learning, Machine learning