Note: this schedule is subject to change. Please check for updates frequently!
| Week | Lecture 1 | Lab | Lecture 2 | Material Covered | Readings |
|---|---|---|---|---|---|
| Aug. 25 | Course Introduction | Lab #1: Custom algorithms in R | Custom algorithms | Review syllabus, programming algorithms in R | Clark Ch. 1; Touchon and McCoy 2016; Bolker Ch 1 |
| Sept. 1 | No class (labor day) | Lab #1: Custom algorithms in R | Custom algorithms | Basic concepts in statistics, probability | Bolker ch. 4 |
| Sept. 8 | Probability | Lab #2: “Virtual Ecologist” | Probability | Probability, mechanistic models, stochastic models | Bolker Chs. 2, 3 |
| Sept. 15 | Data Generating Models | Lab #2: “Virtual Ecologist” | Data Generating Models | Power analysis, goodness-of-fit testing, likelihood | Bolker Ch. 5; Hobbs and Hilborn 2006 (optional) |
| Sept. 22 | Likelihood | Lab #3: Maximum likelihood | Likelihood | Likelihood theory, maximum likelihood | Bolker Ch. 6 |
| Sept. 29 | Likelihood | Lab #3: Maximum likelihood | Optimization | Maximum likelihood inference, optimization algorithms | Bolker Chs. 6, 7 |
| Oct. 6 | Optimization | Group meetings: final projects | Bayesian inference | General introduction to Bayesian theory and application | Bolker Ch. 7 (Bayesian section); Ellison 2004 |
| Oct. 13 | Bayesian inference | Lab #4: Bayesian model fitting | Markov Chain Monte Carlo (MCMC) | Markov-Chain Monte Carlo for Bayesian inference | Bolker Ch. 7 (Bayesian section); |
| Oct. 20 | Model selection and feature elimination | Lab #4: Bayesian model fitting | Model selection re: Bayesian inference | Model selection using information theoretic criteria | Bolker Ch. 6 (AIC section) |
| Oct. 27 | Model performance evaluation | Student project progress reports and discussion | Model performance evaluation | Bias-variance tradeoff, cross-validation, assessing predictive accuracy | |
| Nov. 3 | Multilevel models | Demo/activity: multilevel models in R | Multilevel models | Multilevel models, “Random effects”, non-independence | Bolker Ch. 10 |
| Nov. 10 | Bayesian multilevel models | Demo/activity: multilevel models in stan | Bayesian multilevel models | Bayesian multilevel (hierarchical) models | |
| Nov. 17 | Nonlinear regression, and GAMs | Demo/activity: GAMs in ‘mgcv’ | Nonlinear models and GAMs | Genearlized additive models (GAMs) | |
| Nov. 24 | Spatial models | Demo/activity: Spatial models (INLA?) | Time-series models | Spatial autocorrelation | |
| Dec. 1 | Time-series models | Demo/activity: time-series model | Multivariate statistical models | Temporal autocorrelation | Bolker Ch. 11 |
| Dec. 8 | Class wrap-up- where to go from here | Student project presenations! | No class (prep day) | ||
| Dec. 15 | Project write-ups due by midnight Dec. 15 |