Course Director: Dr. Eugene Demedenko
- Course description: This is a project-driven course and involves three components: theory, real-life data analysis, and R programming for data analysis and computer simulation. We will cover traditional multivariate statistical techniques, such as principal component analysis, canonical correlation, discriminant analysis, hierarchical, hard and soft cluster analysis using Gaussian mixture distribution, multidimensional density estimation. The quality of the classification will be accessed via misclassification error with its connection to the ROC curve. Besides classic multivariate statistical techniques, students will learn advanced methods such as basics of image statistics, pharmacokinetics, and tumor growth analysis. We will discuss identification of objects in images through the bivariate kernel density estimation, statistical detection of synergy, analysis of dose-response relationships, and statistical estimation of the cancer treatment effect. An important feature of the course is uncertainty assessment for building parsimonious and reliable statistical models using machine-learning techniques such as cross-validation. The homework will be assigned each week with a team project as a culminating experience presented at the end of the course.
- Coursework: QBS 129, QBS 121, QBS 177
- Programming: Course work in Calculus, Algebra, and Programming. Intermediate programming experience in R.