| Event | Date | Description | Course Materials |
|---|---|---|---|
| Pre-course |
Saturday October 13 |
Preparation Python tutorials |
Short Python tutorial |
| Lecture 1 |
Sunday October 14 |
Introduction Course overview & logistic |
|
| Lecture 2 |
Sunday October 21 |
Data analysis & visualization 1 NumPy & Matplotlib |
|
| Lecture 3 |
Sunday October 28 |
Population Genetics Discrete-time models for change in allele frequencies |
|
| A1 Due |
Sunday November 04 |
Assignment #1 due Discrete time models |
|
| Lecture 4 |
Sunday November 04 |
Data analysis & visualization 2 Pandas dataframes |
|
| Lecture 5 |
Sunday November 11 |
Statistics Hypothesis testing |
|
| A2 Due |
Sunday November 18 |
Assignment #2 due Statistics |
|
| Lecture 6 |
Sunday November 18 |
Generalized Linear models 1 Regression |
|
| Lecture 7 |
Sunday November 25 |
Generalized Linear models 2 Binomial classification: Logistic model |
|
| A3 Due |
Sunday December 02 |
Assignment #3 due Generalized linear models |
|
| Lecture 8 |
Sunday December 02 |
Population dynamics 1 Deterministic continuous-time models for population growth |
|
| Hanuka |
Sunday December 09 |
No class |
|
| Lecture 9 |
Sunday December 16 |
Population dynamics 2 Deterministic continuous-time models for species interactions |
|
| Lecture 10 |
Sunday December 23 |
Population dynamics 3 Stochastic continuous-time models for molecular dynamics |
|
| Lecture 11 |
Sunday December 30 |
Approximate Bayesian computation Likelihood-free fitting of complex stochastic models |
|
| A4 Due |
Sunday January 06 |
Assignment #4 due Continuous time models |
|
| Lecture 12 |
Sunday January 06 |
Feed forward networks Multinomial classification: Softmax model |
|
| Lecture 13 |
Sunday January 13 |
Convolutional neural networks Multinomial classification: CNN |
|
| A5 Due |
Sunday January 20 |
Assignment #5 due Neural networks |
|
| Project Due |
Thursday February 14 |
Final project due |