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David Danks Department of Philosophy, Carnegie Mellon University; and
 

Summary: Learning
David Danks
Department of Philosophy, Carnegie Mellon University; and
Institute for Human & Machine Cognition
To appear in:
K. Frankish & W. Ramsey (Eds.), Cambridge handbook to artificial intelligence.
Contact info:
Department of Philosophy
135 Baker Hall
Carnegie Mellon University
Pittsburgh, PA 15213
ddanks@cmu.edu
Learning by artificial intelligence systems what I will typically call machine
learning has a distinguished history, and the field has experienced something of a renaissance
in the past twenty years. Machine learning consists principally of a diverse set of algorithms and
techniques that have been applied to problems in a wide range of domains. Any overview of the
methods and applications will inevitably be incomplete, at least at the level of specific
algorithms and techniques. There are many excellent introductions to the formal and statistical
details of machine learning algorithms and techniques available elsewhere (e.g., Bishop, 1995;
Duda, Hart, & Stork, 2000; Hastie, Tibshirani, & Friedman, 2001; Mitchell, 1997). The present

  

Source: Andrews, Peter B. - Department of Mathematical Sciences, Carnegie Mellon University

 

Collections: Mathematics