Course: MATH 445. Statistical Foundations to Machine Learning (3)
Prerequisite: MATH 444 or MATH 440A or graduate standing with approval from the coordinator/instructor. This course will cover concepts of linear models and prediction models including generalized linear models, supervised and unsupervised learning such as classification techniques and clustering. It also includes missing data techniques and concepts of time-series analysis. Software and coding will be used to apply the theory to examples and real data sets. Available for graduate credit.