Schedule

Note: this schedule is subject to change. Please check for updates frequently!

Week Lecture.1 Lecture.2 Lab Final.project.timeline Material.Covered Readings
Aug. 26 Course Introduction Algorithms Lab #1: Programming algorithms in R Start organizing into groups and gathering dataset(s) Review syllabus, algorithmic approach to data analysis, basic programming in R Clark Ch. 1; Touchon and McCoy 2016
Sept. 2 NA (labor day) Algorithms Lab #1: Programming algorithms in R (continued) Basic probability calculus, working with probability distributions Bolker ch. 4; Zurell et al. 2010;
Sept. 9 Probability Probability Final project #1 Organize in groups around project themes and locate suitable data sets for analysis Generating data algorithmically, mechanistic models, power analysis, goodness-of-fit testing Bolker Ch. 1, Ch 5.; Zuur et al. 2010 (optional)
Sept. 16 The Virtual Ecologist Likelihood Lab #2: “Virtual Ecologist” Work on one-page project description Maximum likelihood estimation Bolker Ch. 6; Hobbs and Hilborn 2006 (optional)
Sept. 23 Likelihood Optimization Lab #2: “Virtual Ecologist” (continued) DUE: one-page descriptions of project ideas Optimization algorithms for maximum likelihood inference Bolker Ch. 7
Sept. 30 Optimization Bayesian inference Final project #2 Review proposals with instructor General introduction to Bayesian theory and application Bolker Ch. 6 and 7 (Bayesian section); Ellison 2004
Oct. 7 Bayesian inference Markov Chain Monte Carlo (MCMC) Lab #3: Maximum likelihood Start running analyses and generating figures Markov-Chain Monte Carlo Bolker Ch. 7 and 8
Oct. 14 Markov Chain Monte Carlo (MCMC) Model selection and multi-model inference Lab #3: Maximum likelihood (and digression: graphics in R, generating publication-quality figures) Model selection Anderson et al. 2000; Anderson et al. 2001; Wintle et al. 2003
Oct. 21 Model selection and multi-model inference Model validation and performance evaluation Final project #3 Bias-variance tradeoff, cross-validation, assessing predictive accuracy TBD
Oct. 28 Model validation and performance evaluation Random Forest Lab #4: Bayesian model fitting in JAGS Bias-variance tradeoff, cross-validation, assessing predictive accuracy TBD
Nov. 4 TBD TBD Lab #4: Bayesian model fitting in JAGS (continued) Student-led topics TBD
Nov. 11 NA (veteran’s day) TBD Final project #4 Student-led topics TBD
Nov. 18 TBD TBD Optional: Model selection and performance evaluation (including cross-validation) Student-led topics TBD
Nov. 25 TBD TBD Final project #5 Student-led topics TBD
Dec. 2 TBD TBD Complete “mini-lab” assignments provided by peers Final project complete draft due this week Student-led topics TBD
Dec. 9 Class wrap-up NA (prep day) Final project presentations Final presentations! Student-led topics
Dec. 18 NA (classes over) NA NA Final projects due