ML/COLT tutorial -- Computational Learning Theory Intro & Survey

[Lenny Pitt]

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      Computational Learing Theory: Introduction and Survey
      =========================================================

			Sunday, July 10, 1994
			 8:45 am to 12:15 pm
		        Rutgers University
                      New Brunswick, New Jersey  

			Tutorial conducted by
		            Lenny Pitt
			University of Illinois
			 Urbana, IL 61801
  			 pitt at cs.uiuc.edu

	 Held in conjunction with the Eleventh International
       Conference on Machine Learning (ML94, July 11-13, 1994)
	  and the Seventh Annual Conference on Computational
	     Learning Theory (COLT94, July 12-15, 1994).


This tutorial will introduce the different formal learning models 
(eg, ``pac'' learning, mistake-bounded learning, learning with queries),
present basic techniques for proving learnability and nonlearnability
(eg, the VC-dimension, Occam algorithms, reductions between learning problems),
and survey many of the central results in the area.

The tutorial is designed to give ML attendees and those with a general interest
in machine learning sufficient background to appreciate past and recent 
results in computational learning theory.  It should also help attendees
appreciate the significance and contributions of the papers that will be 
presented at the COLT94 conference that follows.  No prior knowledge of 
learning theory is assumed.

The tutorial is one of a set of DIMACS-sponsored tutorials that 
are free and open to the general public.  
Directions to Rutgers can be found in the ML/COLT announcement, 
which is available via anonymous ftp from www.cs.rutgers.edu in 
the directory "/pub/learning94".   The specific location of the
tutorial will be posted, and available  with  conference materials.
Users of www information servers such as mosaic can find the information at
"http://www.cs.rutgers.edu/pub/learning94/learning94.html".  
Other available information includes a campus map, and abstracts of all
workshops/tutorials.  Questions can be directed to ml94 at cs.rutgers.edu, 
or to colt94 at research.att.com.