STATISTICAL LEARNING AND DATA MINING III
State-of-the-Art Statistical Methods for
Ten Hot Ideas for Learning from Data
September 20, 2014. The class is 54% full, so there are plenty of seats still abailable
This course is the fourth in a series, and follows our popular past
Modern Regression and Classification (1996-2000)
This two-day course gives a detailed overview of statistical models
for data mining, inference and prediction. With the rapid
developments in internet technology, genomics, financial risk
modeling, and other high-tech industries, we rely increasingly more on
data analysis and statistical models to exploit the vast amounts of
data at our fingertips.
Statistical Learning and Data Mining (2001-2005)
Statistical Learning and Data Mining II (2005-2008)
In this course we emphasize the tools useful for tackling modern-day
data analysis problems. From the vast array of tools available, we have selected what we consider are the most relevant and exciting. Our top-ten list of topics are:
- Regression and Logistic Regression (two golden oldies),
Lasso and Related Methods,
Support Vector and Kernel Methodology,
Principal Components (SVD) and Variations: sparse SVD, supervised PCA,
Multidimensional Scaling and Isomap, Nonnegative Matrix Factorization, and Local Linear Embedding,
Boosting, Random Forests and Ensemble Methods,
Rule based methods (PRIM),
Feature Selection, False Discovery Rates and Permutation Tests.
Our earlier courses are not a prerequisite for this new course. Although there is some overlap with past courses, our new course contains many topics not covered by us before.
The material is based on recent papers by the authors and other
researchers, as well as the new second edition of our best selling book:
(with J. Friedman, Springer-Verlag, 2009).
A copy of this book will be given to all attendees.
The lectures will consist of high-quality projected presentations and discussion.
of the SLDM III course at Stanford in October 2014
Registration form for SLDM III course