JSM 2015 Preliminary Program

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CE_21C	Tue, 8/11/2015, 8:30 AM - 5:00 PM	S-Grand Ballroom C
Introduction to Statistical Learning for Unsupervised Problems (ADDED FEE) — Professional Development Continuing Education Course
ASA , Section on Statistical Learning and Data Mining
This course will provide a practical introduction to statistical learning methods for unsupervised problems. We will discuss three classes of methods: cluster analysis, dimension reduction, and graphical modeling. Specifically, we will first discuss hierarchical and K-means clustering methods. We will then discuss principal component analysis and multidimensional scaling as tools for reducing the ambient dimension of the data. Finally, we will discuss sparse graphical models for analysis of high-dimensional data, including data from Gaussian and non-Gaussian distributions. Throughout, we will emphasize practical applications of these methods and their limitations in high-dimensional settings, including validation of results of unsupervised learning methods and tools for reproducible research. We will discuss a number of case studies from finance and biology to describe various statistical learning methods. The course will incorporate material from "Elements of Statistical Learning" by Hastie et al, "Introduction to Statistical Learning" by James et al, and instructor's notes from two courses taught at the Summer Institute for Statistical Genetics (SISG).
Instructor(s): Ali Shojaie, University of Washington

For program information, contact the JSM Registration Department or phone (888) 231-3473.

For Professional Development information, contact the Education Department.

The views expressed here are those of the individual authors and not necessarily those of the JSM sponsors, their officers, or their staff.

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