INTRODUCTION MACHINE LEARNING

← Back to document page

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.

concepts and techniques being explored by researchers in machine learning may illuminate certain aspects of biological learning. As regards machines, we might say, very broadly, that a machine learns

  Machine, Learning, Machine learning, Machine learning may