There will be two team projects in the course --- the first starting in ending in late March, the second ending the last day of classes. Topics for both projects will be listed below. They are drawn from the threads of Modeling Epidemics and Machine Learning. Students can choose to either do both projects from the same thread or do one project from each thread. Because each topic will be assigned to at most one team, it is likely that not everyone will get their first choice of topic. Students who are teammates for the first project will not be teammates for the second project.
Teams are expected to meet regularly outside of class. For each project, the team must submit a written report describing (among other things) the problem that was investigated, the model used and the justification for it, results from analysis and simulation of the model, and the conclusions drawn from the results. To get a better idea of the type of exposition and organization expected, take a look at this sample report for this (briefer) sample project. Teams will also give a brief oral report summarizing their methods and findings. Teams must submit both a paper copy and an electronic copy (PDF) of their written report. Each team member must participate in the team's oral report as well as in its homeworks and written report.
For each team's oral presentation, every student not in the team is expected to submit a paragraph or two on a separate sheet of paper summarizing and critiquing the report: what did you feel were the main points of the report, what was hard to understand, what did you find particularly effective, etc. The quality of your summaries will contribute up to 5 points to your project score.
| Topics for Project Two | Version | Data From | Presentation Date | |
| Modeling Epidemics: | Instructions for All Topics | March 31 | ||
| Uncertainty in Transmission Parameters | March 30 | US and Oakland | May 7 | |
| Uncertainty in Removal Parameters | March 30 | (unassigned) | ||
| Nonlinear Cost Functions | March 30 | US and Miami | May 12 | |
| Time-Varying Interventions | March 30 | US and Philadelphia | May 7 | |
| Machine Learning: | Instructions for All Topics | February 24 | ||
| Machine Learning Project 1 | March 31 | Project One | May 7 | |
| Machine Learning Project 2 | March 31 | Project One | May 5 | |
| Machine Learning Project 3 | March 31 | Project One | May 12 | |
| Machine Learning Project 4 | March 31 | Project One | May 5 |
| Topics for Project One | Version | Presentation Date | |
| Modeling Epidemics: | Instructions for All Topics | March 8 | |
| Forecasting Future Epidemics | February 23 | March 31 | |
| Geographical Dependence of Parameters | February 23 | March 31 | |
| Sensitivity of Parameters to Data | February 23 | March 31 | |
| Improving the Model | February 23 | (not used) | |
| Machine Learning: | Instructions for All Topics | February 24 | |
| Data for All Topics | February 24 | ||
| Machine Learning Project 1 | February 24 | March 26 | |
| Machine Learning Project 2 | February 24 | March 26 | |
| Machine Learning Project 3 | February 24 | March 26 | |
| Machine Learning Project 4 | February 24 | March 24 or 26 | |
| Machine Learning Project 5 | March 5 | (not used) |