[S] Mod reg & class course- final notice

Rob Tibshirani (tibs@stat.stanford.edu)
Mon, 19 Oct 1998 10:22:08 -0700 (PDT)


+++ Modern Regression and Classification: +++
+++ +++
+++ Widely applicable statistical methods +++
+++ for modeling and prediction +++
+++ +++
+++ +++
+++ +++
+++ Chicago, Illinois: Nov. 23-24, 1998. +++
+++ +++
+++ Trevor Hastie, +++
+++ Rob Tibshirani, Stanford University +++
+++ +++

This two-day course will give a detailed overview of statistical models
for regression and classification. Known as machine-learning in
computer science and artificial intelligence, and pattern recognition
in engineering, this is a hot field with powerful applications in
finance, science and industry.

This course covers a wide range of models from linear regression
through various classes of more flexible models to fully nonparametric
regression models, both for the regression problem and for

Although a firm theoretical motivation will be presented, the emphasis
will be on practical applications and implementations. The course will
include many examples and case studies, and participants should leave
the course well-armed to tackle real problems with realistic tools. The
instructors are at the forefront in research in this area.

After a brief overview of linear regression tools, methods for
one-dimensional and multi-dimensional smoothing are presented, as well
as techniques that assume a specific structure for the regression
function. These include splines, wavelets, additive models, MARS
(multivariate adaptive regression splines), projection pursuit
regression, neural networks and regression trees. All of these can be
adapted to the time-series framework for predicting future trends from
the past.

The same hierarchy of techniques is available for classification
problems. Classical tools such as linear discriminant analysis and
logistic regression can be enriched to account for nonlinearities and
interactions. Generalized additive models and flexible discriminant
analysis, neural networks and radial basis functions, classification
trees and kernel estimates are all such generalizations. Other
specialized techniques for classification including nearest- neighbor
rules and learning vector quantization will also be covered.

Apart from describing these techniques and their applications to a wide
range of problems, the course will also cover model selection
techniques, such as cross-validation and the bootstrap, and diagnostic
techniques for model assessment.

Software for these techniques will be illustrated, and a comprehensive
set of course notes will be provided to each attendee.

Additional information is available at the Website:


Some quotes from past attendees:

"... the best presentation by professional statisticians I have
ever had the pleasure of attending"
"Superior to most courses in all aspects"
"I really liked how you emphasized concepts rather than
mathematical expressions"
"Your 2-day course has saved me months of research"



Overview of regression methods: Linear regression models and least
squares. Ridge regression and the ``lasso''. Flexible linear models and
basis function methods. linear and nonlinear smoothers; kernels,
splines, and wavelets. Bias/variance tradeoff- cross-validation and
bootstrap. Smoothing parameters and effective number of parameters.
Non-linear and adaptive time series methods. Surface smoothers.


Structured Nonparametric Regression: Problems with high dimensional
smoothing. Structured high-dimensional regression: additive models.
project pursuit regression. CART, MARS. radial basis functions. neural
networks. applications to time series forecasting.


Classification: Statistical decision theory and classification rules.
Linear procedures: Discriminant Analysis.
Logistic regression. Quadratic discriminant analysis,
parametric models. Nearest neighbor
classification, K-means and LVQ. Adaptive nearest neighbor methods.

The Discrete choice model. Nonparametric classification: Classification
trees: CART. Flexible/penalized discriminant analysis. Multiple
logistic regression models and neural networks. Kernel methods.


Professor Trevor Hastie of the Statistics and Biostatistics
Departments at Stanford University was formerly a member of the
Statistics and Data Analysis Research group AT & T Bell
Laboratories. He co-authored with Tibshirani the monograph Generalized
Additive Models (1990) published by Chapman and Hall, and has many
research articles in the area of nonparametric regression and
classification. He also co-edited the Wadsworth book Statistical
Models in S (1991) with John Chambers.

Professor Robert Tibshirani of the Biostatistics and Statistics
departments at Stanford University is a recent recipient of
the COPSS award - an award given jointly by all the leading
statistical societies to the most outstanding statistician under the
age of 40. He also has many research articles on nonparametric
regression and classification. With Bradley Efron he co-authored the
best-selling text An Introduction to the Bootstrap in 1993, and has
been an active researcher on bootstrap technology for the past 12

Both Prof. Hastie and Prof. Tibshirani are actively involved in
research in modern regression and classification and are well-known
not only in the statistics community but in the machine-learning and
neural network fields as well. The have given many short courses
together on classification and regression procedures to a wide variety
of academic, government and industrial audiences. These include the
American Statistical Association and Interface meetings, NATO ASI
Neural Networks and Statistics workshop, AI and Statistics, and the
Canadian Statistical Society meetings.

Nov 23-24, 1998
Radisson Hotel and Suites Chicago
160 E. Huron St, Chicago.
Phone: (800)-333-3333 or (312) 787 2900

To make room reservations, call the hotel directly. Some rooms have
been blocked off at a special rate for this function.

PRICE: $1200 per attendee. Discounted price of $950- for academic and
non-profit organizations. Cancellation policy: if notification
received by Oct 23, full refund will be given; Oct 23 to Nov 9 -
a 20% administration fee will be charged. After Nov 9-
at the discretion of the instructors. A substitute delegate
is always welcome at no extra charge. Attendance
is limited to the first 60 applicants, so sign up soon! These courses
fill up quickly.


Please print this form, and fill in the hard copy to return by postal
mail or FAX.

Registration by Oct 23 recommended to ensure a spot.


Modern Regression and Classification

Monday and Tuesday, November 23-24, 1998.

Radisson Hotel and Suites Chicago
160 E. Huron St.
Chicago, Illinois 60611
Phone (800)-333-3333
(312) 787 2900

Please complete this form (type or print)

Name ___________________________________________________
Last First Middle

Firm or Institution ______________________________________

Full time student: Yes __ No __

Mailing Address: ____________________________________



Country Phone FAX

email address (PRINT CLEARLY)

__________________________________________ _______________
Visa/MC # (if payment by credit card) Expiration Date

Lunch choice: Vegetarian _____ Non-Vegetarian _____

Please return this form (paper or FAX) by October 23, 1998 to:

Prof. Trevor Hastie
538 Campus Drive
Stanford University
California 94305

For further information, contact:
Prof. Trevor Hastie
Stanford University
Phone/FAX: (650) 326-0854
or tibs@stat.stanford.edu

Rob Tibshirani, Dept of Health Research & Policy
and Dept of Statistics
HRP Redwood Bldg
Stanford University
Stanford, California 94305-5405

phone: HRP: 650-723-7264 (Voice mail), Statistics 650-723-1185
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