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

Ten Hot Ideas for Learning from Data

Sheraton Palo Alto, California - March 19-20, 2015

Prediction Surface

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

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 some of the most useful tools for tackling modern-day data analysis problems. Our top-ten list of topics are:

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)

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.

Software for these techniques will be illustrated, and a copy of the text "Elements of Statistical Learning: data mining, inference and prediction (2nd Edition)" and a comprehensive set of class notes will be provided.

The instructors

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. His Ph.D. thesis Principal Curves introduced one of the first nonlinear versions of principal components analysis. During his ten years at Bell Laboratories he gained valuable experience with classification and regression problems in industry and manufacturing.

Professor Robert Tibshirani of the Biostatistics and Statistics departments at Stanford University is a 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 over the years. His 1984 Ph.D thesis spawned the currently lively research area known as Local Likelihood. He has more than twenty years experience in consulting on biostatistical problems.

This course is based on The Elements of Statistical Learning. This is the 2nd edition (2009) of the best-selling Springer book published in 2001 by Hastie, Tibshirani and Friedman

Professors Hastie and Tibshirani published "The Elements of Statistical learning: Data mining, inference and prediction", with Jerome Friedman (springer, 2001). This book has received a terrific reception, with over 30,000 copies sold. The second edition of this book will appear in February 2009, and has been augmented and brought up to date. Both presenters are actively involved in research in regression, classification and clustering, and are well-known not only in the statistics community but in the machine-learning, neural network and bioinformatics fields as well. In the past 10 years they have become leaders in the statistical analysis of DNA microarrays, working with leading-edge biologists such as Patrick Brown of Stanford University, and David Botstein of Princeton. They have given many short courses together over the past 12 years, to academic, government and industrial audiences. They are both actively involved with consulting in data analysis and modeling, for the Stanford medical community as well as local biotech and web-related industries. They have a reputation for being good instructors who interact well with the needs of the audience.

So far "Statistical Learning and Data Mining III" took place at:

The "Statistical Learning and Data Mining II" course took place at:

The second course "Statistical Learning and Data Mining" by Hastie and Tibshirani took place at

These courses were filled to capacity, and were enthusiastically received by attendees from biotech, financial and other industrial areas.

Their first course - "Modern Regression and Classification" - took place at:

Some quotes from past attendees: