Course Materials

Organized by type:

Course schedule

Topics are subject to change and rearrangement! (We may also not get to all the topics listed)

1. Parameter estimation and maximum likelihood (9/9/19 and 9/11/19)
  • Maximum likelihood with mathematical models - Lecture (9/9/2019)

  • Parameter estimation coding session - Lab (9/11/2019)

2. Math modeling how-to workshop (9/16/19)
  • Main ideas

    • How to go from a question to a diagram to equations to code?

    • What kind of model to use under different circumstances?

  • To Do: Everyone bring a specific question that they might want to use a model to answer, and we’ll workshop it together for the 9/16/19 class.

    • Think about what knowledge about the system you have—what are the causal processes? What features/variables/processes might be important to include to answer your question? What do you think we might be able to ignore or simplify out?

    • Also, if you think you might want to estimate parameters for the model, think a about what kind of data you might want to use! What can you measure/observe, e.g. through epidemiological studies or biological experiments?

  • We will go through each question/problem together and discuss how we might build a model that can answer that question, what the assumptions and limitations of different kinds of models are, etc.

  • Discussion prompts

3. Uncertainty and identifiability

4. Intro to MCMC & Bayesian approaches

5. Digging in to Bayesian estimation for mathematical models

7. Data-scraping and data-wrangling

(moved this up since it might be useful for making data sets we can play with for the ML and networks sections below)

  • Twitter data collection lab

  • Using R to pull data using API’s - Twitter, Facebook, and Google Maps

  • Collecting and working with twitter data

  • An interesting article about the ethics of using public data and our perception of privacy

8. Basic intro to some useful algorithms/machine learning methods

9. Network analysis and network modeling

10. Stochastic models
  • Ideas, Markov models
  • Gillespie algorithm
  • Tau-leaping
  • Possibly discuss hybrid models, journal club some of Linda Allen’s book

11. Additional Topics
  • Game theory and modeling behavior
  • Spatial statistics and spatial/spatiotemporal clustering
  • Analysis/wrangling of text data
  • Optimization algorithms
  • Agent-based modeling