24-786   Special Topics: Bayesian Machine Learning for Scientists and Engineers

Location: Pittsburgh

Units: 12

Semester Offered: Spring

The goal of this course is to introduce bayesian inference starting from first principles. The course will cover efficient current approaches to bayesian modeling and computation and how can they applied to various areas of engineering. The topics that will be covered include bayesian vs frequentist philosophy, Bayes factors, credible intervals, Bayesian Analysis of variance (ANOVA), comparison of means, measurement systems analysis (MSA), control charts, survival/reliability analysis and experiment planning. Undergraduate level understanding of statistics, numerical methods and programming is expected.