SY

Shan Yu

Assistant Professor
Unit: College of Arts and Sciences
Department: Department of Statistics
Office location and address
148 Amphitheater Way
Charlottesville, Virginia 22904
STAT 5330: Data Mining
Credits: 3
This course introduces a plethora of methods in data mining through the statistical point of view. Topics include linear regression and classification, nonparametric smoothing, decision tree, support vector machine, cluster analysis and principal components analysis. Conceptual discussion in lectures is supplemented with hands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisites: Previous or concurrent enrollment in STAT 5120 or STAT 6120.
STAT 5630: Statistical Machine Learning
Credits: 3
Introduces various topics in machine learning, including regression, classification, resampling methods, linear model selection and regularization, tree-based methods, support vector machines, and unsupervised learning. The statistical software R is incorporated throughout. Prerequisite: STAT 5120, STAT 6120, or ECON 3720, and previous experience with R Prerequisite: STAT 5120, STAT 6120, or ECON 3720, and previous experience with R
STAT 6120: Linear Models
Credits: 3
Course develops fundamental methodology to regression and linear-models analysis in general. Topics include model fitting and inference, partial and sequential testing, variable selection, transformations, diagnostics for influential observations, multicollinearity, and regression in nonstandard settings. Conceptual discussion in lectures is supplemented withhands-on practice in applied data-analysis tasks using SAS or R statistical software. Prerequisite: Graduate standing in Statistics, or instructor permission.
STAT 9999: Non-Topical Research
Credits: 1–12
For doctoral research, taken under the supervision of a dissertation director.