Capstone lessons

The goal of these lessons is to see how the concepts covered in the main lessons can be integrated. It is recommended to go through the capstones after having done the lessons. Capstones are designed to be shorter, and will present some real-life applications of the principles covered in the main lessons.

Capstone lessons present more advanced material, which may take a bit longer to grasp. We encourage you to try them one at a time, and to come back to them over the course of a few days. We also encourage you to adapt the capstones to your own problems, and to see if they can adapted to your own data. This will be a frustrating process, to be sure, but you will learn a lot!

Approximate Bayesian Computation

Reading time: 18 minutes
Contributors: Timothée Poisot
Key concepts: arrays control flow writing functions
Packages used: StatsPlots Statistics Distributions

Genetic algorithm

Reading time: 11 minutes
Status: draft
Contributors: Timothée Poisot
Key concepts: data frames generic code type system
Packages used: StatsPlots CSV DataFrames Statistics

Runge-Kutta integration

Reading time: 11 minutes
Contributors: Timothée Poisot
Key concepts: writing functions numerical precision arrays keyword arguments
Packages used: StatsPlots