| Event | Date | Description | Course Materials |
|---|---|---|---|
| Preparation |
Thursday May 23 |
Preparation Tutorials |
Book: Think Python |
| Lecture 1 |
Thursday May 30 |
Introduction Models in Biology Or: Biology is more theoretical than physics |
|
| A0 Due |
Thursday June 06 |
Assignment #0 due Python, NumPy, Matplotlib, Pandas, Seaborn |
|
| Lecture 2 |
Thursday June 06 |
Population Dynamics 1 Continuous-time univariate deterministic model: Population growth models |
|
| Lecture 3 |
Thursday June 13 |
Population Dynamics 2 Continuous-time multivariate deterministic model: Predator-prey model |
|
| A1 Due |
Wednesday June 19 |
Assignment #1 due Continuous-time deterministic models |
|
| Lecture 4 |
Thursday June 20 |
Population Genetics 1 Discrete-time univariate deterministic model: Haploid selection |
|
| Lecture 5 |
Thursday June 27 |
Population Genetics 2 Discrete-time stochastic model: Wright-Fisher model |
|
| Lecture 6 |
Thursday July 04 |
Epidemiology Continuous-time stochastic model: SIR model |
|
| A2 Due |
Wednesday July 10 |
Assignment #2 due Discrete-time deterministic models |
|
| Lecture 7 |
Thursday July 11 |
Statistical Inference 1 Maximum likelihood estimation: Count data |
|
| Lecture 8 |
Thursday July 18 |
Statistical Inference 2 Bayesian inference: Count data |
|
| A3 Due |
Wednesday July 24 |
Assignment #3 due Stochastic models |
|
| Lecture 9 |
Thursday July 25 |
Generalized Linear Models 1 Generalized linear models 1: Exponential growth |
|
| Proposal Due |
Wednesday July 31 |
Proposal due |
|
| Lecture 10 |
Thursday August 01 |
Generalized Linear Models 2 Generalized linear models 2: COVID-19 survival |
|
| Lecture 11 |
Thursday August 08 |
Statistical Inference 3 Bayesian inference in deterministic models: Predator-prey model |
|
| Lecture 12 |
Thursday August 08 |
Statistical Inference 4 Likelihood-free inference: Animal social networks |
|
| A4 Due |
Wednesday August 14 |
Assignment #4 due Statistical inference |
|
| Project Due |
Sunday October 13 |
Final project due |