Learning Methods for Prediction, Classification,
Marney Smyth
marney at ai.mit.edu
Sun Nov 10 16:34:12 EST 1996
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*** Learning Methods for Prediction, Classification, ***
*** Novelty Detection and Time Series Analysis ***
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*** Los Angeles, CA, December 14-15, 1996 ***
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*** Geoffrey Hinton, University of Toronto ***
*** Michael Jordan, Massachusetts Inst. of Tech. ***
*** ***
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A two-day intensive Tutorial on Advanced Learning Methods will be held
on December 14 and 15, 1996, at Loews Hotel, Santa Monica, CA. Space
is available for up to 50 participants for the course.
The course will provide an in-depth discussion of the large collection
of new tools that have become available in recent years for developing
autonomous learning systems and for aiding in the analysis of complex
multivariate data. These tools include neural networks, hidden Markov
models, belief networks, decision trees, memory-based methods, as well
as increasingly sophisticated combinations of these architectures.
Applications include prediction, classification, fault detection,
time series analysis, diagnosis, optimization, system identification
and control, exploratory data analysis and many other problems in
statistics, machine learning and data mining.
The course will be devoted equally to the conceptual foundations of
recent developments in machine learning and to the deployment of these
tools in applied settings. Case studies will be described to show how
learning systems can be developed in real-world settings. Architectures
and algorithms will be presented in some detail, but with a minimum of
mathematical formalism and with a focus on intuitive understanding.
Emphasis will be placed on using machine methods as tools that can
be combined to solve the problem at hand.
WHO SHOULD ATTEND THIS COURSE?
The course is intended for engineers, data analysts, scientists,
managers and others who would like to understand the basic principles
underlying learning systems. The focus will be on neural network models
and related graphical models such as mixture models, hidden Markov
models, Kalman filters and belief networks. No previous exposure to
machine learning algorithms is necessary although a degree in engineering
or science (or equivalent experience) is desirable. Those attending
can expect to gain an understanding of the current state-of-the-art
in machine learning and be in a position to make informed decisions
about whether this technology is relevant to specific problems in
their area of interest.
COURSE OUTLINE
Overview of learning systems; LMS, perceptrons and support vectors;
generalized linear models; multilayer networks; recurrent networks;
weight decay, regularization and committees; optimization methods;
active learning; applications to prediction, classification and control
Graphical models: Markov random fields and Bayesian belief networks;
junction trees and probabilistic message passing; calculating most
probable configurations; Boltzmann machines; influence diagrams;
structure learning algorithms; applications to diagnosis, density
estimation, novelty detection and sensitivity analysis
Clustering; mixture models; mixtures of experts models; the EM
algorithm; decision trees; hidden Markov models; variations on
hidden Markov models; applications to prediction, classification
and time series modeling
Subspace methods; mixtures of principal component modules; factor
analysis and its relation to PCA; Kalman filtering; switching
mixtures of Kalman filters; tree-structured Kalman filters;
applications to novelty detection and system identification
Approximate methods: sampling methods, variational methods;
graphical models with sigmoid units and noisy-OR units; factorial
HMMs; the Helmholtz machine; computationally efficient upper
and lower bounds for graphical models
REGISTRATION
Standard Registration: $700
Student Registration: $400
Cancellation Policy: Cancellation before Friday December 6th, 1996,
incurs a penalty of $150.00. Cancellation after Friday December 6th,
1996, incurs a penalty of one-half of Registration Fee.
Registration Fee includes Course Materials, breakfast, coffee breaks,
and lunch on Saturday December 14th.
On-site Registration is possible. Payment of on-site registration must
be in US Dollar amounts, by Money Order or Check (preferably drawn on
a US Bank account).
Those interested in participating should return the completed
Registration Form and Fee as soon as possible, as the total number of
places is limited by the size of the venue.
Please print this form, and fill in the hard copy to return by mail
REGISTRATION FORM
Learning Methods for Prediction, Classification,
Novelty Detection and Time Series Analysis
Saturday, December 14 - Sunday, December 15, 1996
Santa Monica, CA, USA.
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Please complete this form (type or print)
Name ___________________________________________________
Last First Middle
Firm or Institution ______________________________________
Standard Registration ____ Student Registration ____
Mailing Address (for receipt) _________________________
__________________________________________________________
__________________________________________________________
__________________________________________________________
Country Phone FAX
__________________________________________________________
email address
(Lunch Menu, Saturday December 14th - tick as appropriate):
___ Vegetarian ___ Non-Vegetarian
[Image]
Fee payment must be made by MONEY ORDER or PERSONAL CHECK. All amounts
are given in US dollar figures. Make fee payable to Prof. Michael
Jordan. Mail it, together with this completed Registration Form to:
Professor Michael Jordan
Dept. of Brain and Cognitive Sciences
M.I.T.
E10-034D
77 Massachusetts Avenue
Cambridge, MA 02139
USA
HOTEL ACCOMMODATION
Hotel accomodations are the personal responsibility of each participant.
The Tutorial will be held in
Lowes Santa Monica Beach Hotel,
1700 Ocean Avenue
Santa Monica CA 90401
(310) 458-6700 FAX (310) 458-0020
on December 14 and 15, 1996.
The hotel has reserved a block of rooms for participants of the course. The
special room rates for participants are:
U.S. $170.00 (city view) per night + tax
U.S. $250.00 (full ocean view) per night + tax
Please be aware that these prices do not include State or City taxes.
Participants may wish to avail of discounted overnight parking rate of
$13.30 (self) and $15.50 (valet).
ADDITIONAL INFORMATION
A registration form is available from the course's WWW page at
http://www.ai.mit.edu/projects/cbcl/web-pis/jordan/course/index.html
Marney Smyth
Phone: 617 258-8928
Fax: 617 258-6779
E-mail: marney at ai.mit.edu
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