>> Register here by January 12, 2019
The course gives an introduction to the relatively young Julia programming language. The core goal of the language is to combine the expressiveness dynamism of languages like R, Python and matlab with the speed of Fortran or C.
The course is targeted at people with some programming experience in another programming language who are interested to dive into an adventure and explore different ways to get your research done. The course will focus on explaining the differences between Julia and other common scientific programming languages and how this enables new ways to implement common methods in data analysis and modelling. Emphasis will be laid on how to write well-structured and performant code in Julia. Day 1 of the course will focus on introducing the language, while day 2 and 3 will be in a workshop-format to get an introduction to the package ecosystem and to try the newly learned concepts hands-on.
Basic setup of your Julia environment, packages, plotting, editors.
Case study on solving differential equations. We start with a very simple manual ODE solver and then switch to the DifferentialEquations.jl package and try to solve a differential equation parameter optimization problem.
Case study on deep learning und Flux.jl. We start with basic neural networks and extend them by designing our own layers.
For questions or topic suggestions, please contact Fabian Gans. If you want to bring your own data/model/problem to the course and want to work on a problem on day 2 or 3, please contact the instructor and we will discuss the feasibility for the course.
Bring a laptop.
Please also make sure that you can access the internet via WLAN (BGC-users, if you have a BGC-account; BGC-guests, if you don't have an account)
Click here to register before January 12, 2019.
This page was last modified on November 15, 2018, at 03:36 PM