# Robert B. Gramacy Professor of Statistics

# Teaching

In Fall Semester 2017 I am teaching a Ph.D. level class on Advanced Statistical Computing, and an undergraduate class on Nonparametric Statistics. Both will be taught through the Department of Statistics at Virginia Tech.

## Advanced Statistical Computing

**STAT 6984**
is a second (graduate) course on statistical computing. Although basics will be revisited,
the pace will be swift. The main programming language will be
`R`

a>,
but we will explore many other languages and tools. We will learn how statisticians can best leverage modern
desktop computing (multiple cores), cluster computing (multiple nodes) and distributed computing (hadoop/Amazon EC2). An aspect of that preparation will be "back to basics" with navigating the Unix shell,
manipulating data therein, compiling libraries with make, version control
(e.g., Git), and good habits/best practice with code development and data management.

Course Syllabus Class Page

## Nonparametric Statistics

**STAT 3504**
is an undergruadate course focused on statistical methodology based on ranks, empirical distributions, and runs.
One and two sample tests, ANOVA, correlation, goodness of fit, rank regression, R-estimates and confidence intervals.
We will learn comparisons with classical parametric methods. There will be an emphasis on assumptions and interpretation.
It is targeted towards students who have completed (and remember the concepts from) a course in introductory statistics.
We will make extensive use of computational tools, such as the `R`

language
for statistical computing, both for illustration in class and in homework problems.

Course Syllabus Class Page

In the 2016-2017 academic term I taught class on Intermediate Data Analytics and Machine learning, cross listed with the Computer Science and Computational Modeling and Data Analytics majors, and a Ph.D. level class on Response Surface Methods. Both courses were taught through the Department of Statistics at Virginia Tech.

## Int. Data Analytics & Machine Learning

**CMDA/CS/STAT 4564**
is a technical analytics course that will teach supervised and unsupervised learning strategies, including regression, generalized linear models, regularization, dimension reduction methods, tree-based methods for classification, and clustering. Upper-level analytical methods are shown in practice: e.g., advanced naïve Bayes, neural networks and Gaussian processes. It is targeted towards students who have completed (and remember the concepts from) a course in introductory statistics and mathematical modeling.
We will make extensive use of calculus, linear algebra, and probability.
Computational tools, such as the `R`

language
for statistical computing, will be used for illustration in class and be essential for completing homework problems.

Course Syllabus Class Page

## Response Surface Methods & Computer Experiments

**STAT 6984** is a graduate "topics" statistics course at the interface between mathematical modeling via
computer simulation, computer model meta-modeling (i.e., emulation/surrogate modeling), calibration of
computer models to data from field experiments, and model-based sequential design and optimization under
uncertainty. The treatment will include some of the historical methodology in the literature, and canonical
examples, but will concentrate on modern statistical methods, computation and implementation in
`R`

, as motivated by modern application/data type and size.

Course Syllabus Class Page

During the 2015-2016 academic year I taught one section of a Ph.D. level class on Bayesian Inference, and two sections of an MBA level class on Applied Regression Analysis, both in the Spring Quarter within the Booth School of Business at the University of Chicago. See my CV for a more complete teaching record.

## Bayesian Inference

**BUS 41913** is a graduate course in Bayesian Inference. The course will focus on understanding the principles underlying Bayesian modeling and on building experience in the use of Bayesian analysis for making inference about real world problems. Particular attention will be paid to the computational techniques (e.g., MCMC) needed for most problems and their implementation in the `R`

language for statistical computing.

Course Syllabus Class Page

## Applied Regression Analysis

**BUS 41100** (Sections 01, 02 and 085) is a course about regression, a powerful and widely used data analysis technique. Students will learn how to use regression to analyze a variety of complex real world problems. Heavy emphasis will be placed on analysis of actual datasets, and implementation in the `R`

language for statistical computing. Topics covered include: simple linear regression, multiple regression, prediction, variable selection, residual diagnostics, time series (auto-regression), and classification (logistic regression).

Course Syllabus Class Page