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

Maximum likelihood with mathematical models - Lecture (9/9/2019)

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

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.

Identifiability and parameter uncertainty - Lecture

Useful post on Wald/FIM vs. Likelihood-based confidence intervals

Intro to Bayesian Approaches to Parameter Estimation, MCMC - Lecture

Intro to ggplot and collection of examples: Lab - our collection of ggplot examples, Dataset

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

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

- Overall topics of interest: k-means clustering, support vector machines, singular value decomposition (possibly a little linear algebra review also?), basic neural networks
- Clustering Methods Lecture
- Density-sensitive version of DBSCAN: OPTICS
- Another overview of DBSCAN and some of its variants

- Clustering Lab
- Other useful tutorials:

- Classification
- Lab: Naive Bayes classification with twitter data
- Twitter topic datasets: Aliens, Vaccines, Vaccines 2
- Other useful tutorials
- Naive Bayes in R for movie reviews
- Naive Bayes with Titanic survivorship
- Naive Bayes Overview - includes more details on cross validation, confusion matrices, Laplace smoother, etc.

- Networks lecture
- Review of network concepts (centrality, modularity, etc.)
- Network simulation methods (Barabasi-Albert, Erdos-Renyi, etc.)
- Configuration models

- Network data sets
- Mark Newman’s list of data sets
- UC Irvine Network Data Repository
- Facebook social network data from kaggle
- Colorado network data set
- Transitland: transit data for major cities and visualization packages for it: transitland github, visualizations 1, more visualizations

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

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

- 10/30/19 Journal club topic: Dissecting racial bias in an algorithm used to manage the health of populations
- 11/6/19 Journal club: Measles virus infection diminishes preexisting antibodies that offer protection from other pathogens
- 11/13/19 Journal Club:
- 12/2/19 Journal club: The dynamic nature of contact networks in infectious disease epidemiology