FIRST CALL FOR PARTICIPATION

Two Masterclasses in Maastricht

In the week of BNAIC 99 SIKS organizes two masterclasses in Maastricht, on
November 1 and 2. Both masterclasses will be given in English and are part
of the Educational Program for SIKS-Ph.D. Students. (See appendix 1)
Although they are primarily intended for SIKS-Ph.D. Students, other
participants are not excluded. However, their number of passes will be
restricted and depends on the number of students taking the class.

"Heuristic Search: Tools of the Trade"  
Monday November 1. 14.00 - 17.00 hrs
by Jonathan Schaeffer (University of Alberta)

"An overview of Machine Learning"
Thuesday November 2. 9.30 - 12.00 hrs
by Tom Mitchell (Carnegie Mellon University)

Participants will not be billed, but an early registration is required. If
you are interested in taking the masterclasses,
please inform the Office of SIKS, before October 23 and preferably by
e-mail: office@siks.nl  or,
if so desired, by telephone. Please contact Richard Starmans (030-2534083)
for further questions.
 
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Jonathan Schaeffer
Heuristic Search: Tools of the Trade

Search is an integral part of most Artificial Intelligence systems.
There are a myriad of search algorithms, each applicable for a
(possibly small) class of applications. Usually, the algorithm choice
is easy; it is determined by properties of the application. Most
of the effort in developing high-performance search engines is in
the enhancements to the search algorithm. An appropriate set of
enhancements can reduce a search tree size by many orders of 
magnitude.
Most AI textbooks concentrate on the different search algorithms
and fail to give much attention to the algorithm enhancements. This
talk will concentrate on the enhancements, with only minor attention
to the search algorithms. If one studies the development of a high
performance search engine, one finds that typically 1% of the effort
goes into the choice and implementation of an appropriate search
algorithm; 99% goes towards the selection, implementation and tuning
of the enhancements. This talk discusses search algorithm enhancements
that have benefits across a wide range of search algorithms.



Tom M. Mitchell
An Overview of Machine Learning

Machine Learning is a field of computer science that is concerned with 
the question "how can we design computer programs that automatically 
improve through experience?"  This class will survey a number of the key 
algorithms in machine learning, the theory underlying these, and 
applications to problems such as learning to predict medical outcomes, 
learning to recognize faces, and learning to classify web pages.

Copies of lecture slides are available at
www.cs.cmu.edu/~tom/tutorial-siks99-x4.ps


Appendix 1
Both masterclasses are part of the so-called "advanced components stage" of
SIKS's educational program. SIKS offers a four-year Ph.D.-program which
intends to provide students with broad basic knowledge as well as
specialized advanced training. In order to reach these aims, the school has
developed a pyramid model, that comprises five stages: homogenization, the
basic course program, advanced components, general research skills and
independent research. 
The homogenization stage is especially intended to raise the knowledge of
beginning SIKS-students to a common level. This can be achieved by
independent study or by taking undergraduate courses at some department or
faculty in the Netherlands. It should not exceed a period of 4 weeks in the
first year of the appointment. The goal of the Basic Course Program, the
second stage of the educational program, is to bring the student's general
knowledge of the field of Information and Knowledge Systems to an
international level. The core of the program is a two-yearly cycle of basic
courses, that cover important paradigms, topics and trends in the
beforementioned field. Unlike the courses that can be taken in the
homogenization stage, the basic courses are entirely developed by
SIKS-staff members and they primarily focus on the Ph.D-students of the
school. The basic courses are an obligatory part of the education and
supervision plan of each SIKS- Ph.D. student. They are supposed to take at
least 6 out of  8 courses. 
The beforementioned masterclasses on heuristic search and machine learning
belong to the third stage of the program. This "advanced components stage"
comprises divergent activities and miscellaneous subjects. The main aim is
to support students' research and  allow them to gain  in-depth knowledge
on a particular field. The activities of this third stage can be courses or
masterclasses, but also participation in reading groups or seminars.