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Courses

Spring 2017

STA613/CBB540: Statistical methods in computational biology
STA230: Probability

Undergraduate

Sta561/CompSci571: Probabilistic Machine Learning
Fall 2015
Sta 113- Probability/Statistics in engineering
Fall 2006, 2007, 2008, 2009
Introduction to probability, independence, conditional independence, and Bayes' theorem. Discrete and continuous, univariate and multivariate distributions. Linear and nonlinear transformations of random variables. Classical and Bayesian inference, decision theory, and comparison of hypotheses. Experimental design, statistical quality control, and other applications in engineering.

Sta 114/Math 136- Statistical inference
Spring 2008
An introduction to the concepts, theory, and application of statistical inference, including the structure of statistical problems, probability modeling, data analysis and statistical computing, and linear regression. Inference from the viewpoint of Bayesian statistics, with some discussion of sampling theory methods and comparative inference. Applications to problems in various fields.

Sta 180S- Statistical Methods in Bioinformatics
Spring 2009
Explore statistical models and analytical tools for bioinformatics and genomics. Topics include functional inference for DNA, RNA, and protein sequences, and the analysis of genetic pedigrees, gene expression experiments, and families of molecular sequences and structures.

Graduate

Sta561/CompSci571: Probabilistic Machine Learning
Fall 2015
STA 571 COMPSI571- Probabilistic Machine Learning
Fall 2014
Introduction to concepts in probabilistic machine learning with a focus on discriminative and hierarchical generative models. Topics include directed and undirected graphical models, kernel methods, exact and approximate parameter estimation methods, and structure learning.

STA 205- Probability and measure theory
Fall 2009
This is a course about random variables, especially about their convergence and conditional expectations, providing an introduction to the foundations of modern Bayesian statistical inference. Students are expected to know real analysis at the level of W. Rudin's Principles of Mathematical Analysis or M. Reed's Fundamental Ideas of Analysis--- the topology of, convergence in metric spaces (especially uniform convergence of functions on), infinite series, countable and uncountable sets, compactness and convexity, and so forth. Students without this background should take or at least co-register with Duke's Math 203, Basic Analysis I. More advanced mathematical topics from real analysis, including parts of measure theory, Fourier and functional analysis, are introduced as needed to support a deep understanding of probability and its applications. Topics of later interest in statistics (e.g., regular conditional density functions) are given special emphasis.

CBB 240/STA 270- Statistical methods in computational biology
Spring 2006, 2007, 2009, 2010, 2012
Methods of statistical inference and stochastic modeling with application to functional genomics and computational molecular biology. Topics include: statistical theory underlying sequence analysis and database searching; Markov models; elements of Bayesian and likelihood inference; multivariate high-dimensional regression models, applied linear regression analysis; discrete data models; multivariate data decomposition methods (PCA, clustering, multi-dimensional scaling); software tools for statistical computing.

Topics in probability: random graphs and statistical inference
Fall 2007
Probabilistic and statistical aspects of random graphs. Specifically structure of random graphs and statistical inference on graphs.