Statistical and Machine Learning Methods for Genomics
BIO-268 / STATS-345 / CS-373 / GENE-245 / BIOMEDIN-245

Instructors: Hua Tang, Anshul Kundaje, and Jonathan Pritchard
Introduction to statistical and machine learning methods for genomics. Sample topics include: expectation maximization, Hidden Markov models, Markov chain Monte Carlo, ensemble learning (Boosting, Random Forests), basic probabilistic graphical models, Support Vector Machines and Kernel Methods and other modern machine learning paradigms such as Deep Learning. Rationales and techniques illustrated with existing implementations used in population genetics, disease association, and functional regulatory genomics studies. Last taught in the Spring 2015.

Introduction to Statistical Genetics
GENE 244

Statistical methods for analyzing human genetics studies of Mendelian disorders and common complex traits. Probable topics include: principles of population genetics; epidemiologic designs; familial aggregation; segregation analysis; linkage analysis; linkage-disequilibrium-based association mapping approaches; and genome-wide analysis based on high-throughput genotyping platforms. Last taught in the Spring 2014.

Computational Algorithms in Genetics
GENE 245/ STATS 345/ STATS 166

Computational algorithms for human genetics research. Topics include: permutation, bootstrap, expectation maximization, hidden Markov model, and Markov chain Monte Carlo. Rationales and techniques illustrated with existing implementations commonly used in population genetics research, disease association studies, and genomics analysis. Last taught in the Spring 2013.

Consulting Workshop

Skills required of practicing statistical consultants, including exposure to statistical applications. Students participate as consultants in the department's drop-in consulting service, analyze client data, and prepare formal written reports. Seminar provides supervised experience in short term consulting. Last taught in the Spring 2013.