Applied Modern Multivariate Statistical Learning

STAT 502 XW


Course Description

A Statistics-MS-level introduction to Modern Multivariate Statistical Learning. Theory-based methods for modern data mining and machine learning, inference and prediction. Variance-bias trade-offs and choice of predictors; linear methods of prediction; basis expansions; smoothing, regularization, kernel smoothing methods; neural networks and radial basis function networks; bootstrapping, model averaging, and stacking; linear and quadratic methods of classification; support vector machines; trees and random forests; boosting; prototype methods; unsupervised learning including clustering, principal components, and multi-dimensional scaling; kernel mechanics. Substantial use of R packages implementing these methods.

3 credits

Questions

Instructor Contact

Steve Vardeman

Registration Information

Course Dept
STAT
Course Number
502
Section
XW
Credit Hours
3
Semester
Spring 2018
Dates
Jan 8 - May 4
Prerequisites
STAT 500, STAT 542, STAT 579
Max Enrollment
50
Delivery Fee
$150.00 *
* Delivery Fee is additional to Tuition & Fees.

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