This course uses a Case-Study approach to teach statistical techniques with R: confidence intervals, hypotheses tests, regression, and anova. Also, it covers major statistical learning techniques for both supervised and unsupervised learning. Supervised learning topics cover regression and classification, and unsupervised learning topics cover clustering & principal component analysis. Prior basic statistic skills are needed. Prerequisite: Engineering Undergraduate and APMA 3100 or APMA 3110.

Topics vary from year to year and are selected to fill special needs of graduate students.

Applies mathematical techniques to special problems of current interest. Topic for each semester are announced at the time of course enrollment.