STAT 6115: Statistical Machine Learning with Big Data

University of North Carolina, Charlotte

A survey of major statistical learning concepts and methods for big data analysis, including both supervised and unsupervised learning such as resampling methods, support vector machines, model selection and regularization, tree-based methods and ensembles, and statistical graphics. �Students learn how and when to apply statistical learning techniques, their comparative strengths and weaknesses, and how to critically evaluate the performance of learning algorithms in case studies in financial investment, gene identification, and feature selection in high-dimensional spaces.

Average GPA: 3.77

Grade distribution records: 95 students across 11 terms.

Grade distribution

Based on 95 student grade records across 11 terms and 2 professors.

Instructors

• Jiancheng Jiang 74 students, Average GPA 3.74
• Jun Song 21 students, Average GPA 3.90