State-of-the-Art Statistical Methods for Data Analysis:

Ten Hot Ideas for Learning from Data

Executive Conference Center, New York - October 8-9, 2015


Prediction Surface

A short course given by
Trevor Hastie and Robert Tibshirani
both of Stanford University

This course is the fourth in a series, and follows our popular past offerings:

Modern Regression and Classification (1996-2000)

Statistical Learning and Data Mining (2001-2005)

Statistical Learning and Data Mining II (2005-2008)

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.

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:

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:

Elements of Statistical Learning: data mining, inference and prediction (2nd Edition)

(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.

  • Further details of the SLDM III course in New York in October 2015
  • Registration form for SLDM III course