Schedule

Schedule

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

Week	Lecture 1	Lab	Lecture 2	Readings
Aug. 24	Course Introduction	Lab 1: Data visualizations, math+stats prerequisites	logarithms and exponents	Bolker Ch 1-3, Bolker appendix 1
Aug. 31	Working with functions	Lab 2: Working with functions	Calculus review	Bolker Ch. 3, Bolker appendix 2-4
Sept. 7	Probability	Lab 3: Working with probabilities	Probability distributions, bayesian stats	Bolker Ch. 4
Sept. 14	Simulating data	Final project proposals- getting into groups	Working with matrices	Bolker Ch. 5, Bolker appendix 8, Deep learning book ch 1
Sept. 21	Ordinary least squares	Lab 4: Data generating models	Orthogonization, rotations	Simon Wood textbook?
Sept. 28	Maximum likelihood estimation	Lab 5: Maximum likelihood estimation	Maximum likelihood estimation	Bolker Ch. 6,
Oct. 5	More likelihood	Lab 6: More likelihood!	Optimization	Bolker Chs. 6, 7
Oct. 12	Optimization	Group meetings: final project proposals	Markov Chain Monte Carlo (MCMC)	Bolker Ch. 7
Oct. 19	Bayesian inference	Lab 7: Bayesian inference	Model selection	Bolker Ch. 7
Oct. 26	Feature selection	Lab 8: Feature selection	Bayesian model selection	Bolker Ch. 6.8
Nov. 24	Modeling variance	Lab 9: Modeling variance	No class (veteran’s day)	Bolker Ch. 10
Nov. 9	Bayesian multilevel models	Lab 10: Bayesian multilevel models	Bayesian multilevel models	Bolker Ch. 10
Nov. 16	Nonlinear regression and GAMs	Group meetings: final project results	Nonlinear regression and GAMs
Nov. 23	Spatial regression models	Lab 11: Spatial regression models	Spatial regression models
Nov. 30	Dynamic models	Lab 12: Dynamic models	Dynamic models	Bolker Ch. 11
Dec. 7	Class wrap-up- where to go from here	Student project presenations!	No class (prep day)
Dec. 14	Project write-ups due by midnight Dec. 14