Bayesian methods are increasingly used to analyze data in ecology and related disciplines and have some advantages over classical (frequentist) methods many scientists are familiar with. The aim of this workshop is to introduce participants to Bayesian statistics, with the expectation that participants will become better-equipped consumers and producers of scientific inference based on Bayesian methods of data analysis. The workshop will focus on applied tutorials built around simple and familiar statistical models. Discussions of the underlying philosophy, computational methods, and mathematical theory will be brief. Participants will use Bayesian methods to calculate basic summary statistics (mean, proportion), then advance to fitting Bayes regression models (linear regression, generalized linear models [GLM], and mixed models [GLMM]. Hands-on exercises will be run in Program R using its interface to JAGS. We expect that participants will leave with an increased fluency to evaluate research based on Bayesian statistics and with an increased capacity to learn and apply Bayesian methods in their own research.

Machine learning is a useful tool for ecologists to manage and analyze data. This introductory workshop will provide the skills needed to begin working with data, and training and evaluating machine learning algorithms in python. We will include a brief introduction to python and anaconda, data management, regression, classification, random forests, model selection, and more. While theoretical introductions to each topic will be provided, the main focus will be on practical applications, as workshop participants will be programming along with instructors. Following this workshop, participants will be ready for our next workshop in artificial neural networks.