Search Results

STAT 712. Applied Statistical Machine Learning. 3 Credits.

This course provides several fundamental concepts and methods in statistical machine learning: linear method for regression, linear method for classification, KNN, regression tree, classification tree, bagging, random forest, boosting, support vector machine, neural networks, K-means clustering. Knowledge of basic inferential statistical methods is expected. Restriction: This course is one of the courses for the Certificate of Applied Big Data Analysis and it may not be used for the M.S. or Ph.D. in Statistics.


The Department of Statistics offers programs leading to a Doctor of Philosophy (Ph.D.) in Statistics, a Master of Science (M.S.) degree in Applied Statistics, and certificates in Statistics (for non-majors) and Big Data Applied Statistics Analysis. The program is flexible enough create a plan based on individual prior experience and in accord with professional goals. During the first year of the program, master's and doctoral students are strongly encouraged to meet with each faculty member to discuss possible research topics. The student should select a supervisory committee by the end of the first year. Graduate certificates in Statistics for non-majors and Big Data Applied Statistics Analysis are also available.