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STAT 712. Applied Statistical Machine Learning. 3 Credits.
This course provides several fundamental concepts and methods in statistical machine learning and big data analysis: divide and conquer, parallel computing in R, linear method for regression, lasso, linear method for classification, logistic regression, KNN, model selection and assessment, regression tree, classification tree, bagging, random forest, boosting, support vector machine, neural networks, K-means clustering, principal components analysis. We use R to implement all the methods in this course. NOTE: It cannot be taken as credit towards M.S. in Applied Statistics or the Ph.D. degree or the Graduate Certificate in Statistics, but may be taken as credit for the Big Data Statistical Analysis Graduate Certificate. This course is also part of the M.S. degree program in Data Science.
Cross-listed with DATA 712.
Statistics
http://catalog.ndsu.edu/programs-study/graduate/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.