Courses

Short course: Statistical Learning and Data Mining III:
Ten Hot Ideas for Learning from Data
www-stat.stanford.edu/~hastie/sldm.html

Trevor Hastie and Robert Tibshirani, Stanford University

Sheraton Hotel
Palo Alto, CA
March 16-17, 2009

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:

• 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),
• Graphical Models,
• Cross-Validation,
• Bootstrap,
• Feature Selection, False Discovery Rates and Permutation Tests.

http://www-stat.stanford.edu/~hastie/sldm.html