MATH 253 - Statistical Computing and Machine Learning

Statistics as applied to “big data,” including large numbers of variables. The linear and logistic modeling techniques from Math 155 will be augmented with computer-based methods of data exploration, visualization, data mining, supervised and unsupervised clustering, discriminant analysis, contemporary approaches to the problem of overfitting, and dimension reduction. The course also deals with methods of combining and organizing data from diverse sources and the high-level statistical computer programming needed to carry out sophisticated data analysis. Prerequisite(s): MATH 155

Once per year starting in 2015-2016. (4 Credits)