MW 2:30-4:00 4-237


Julia is increasingly being recognized for the development of machine learning software. The 2017 course will focus a great deal on what this is about. This year's projects will likely be less scientific based and more machine learning based. We will still cover parallelism, GPUs, and performance issues as in previous years but updated for the modern world.

2016 course poster


A fresh approach to technical computing: the Julia programming language.
High performance and parallelism from high-level code.

Getting started!

  1. Create an account on Github and add your SSH key
  2. Fillup the signup form
  3. Signup to Piazza
  4. Install Julia 0.6 on your Laptop (Platform specific instructions)
  5. We recommend using VSCode with Julia and/or Jupyter


Most material will be populated on the course GitHub page .

Introduction to Julia

Take a look at the excellent material provided by Prof. Steven G. Johnsson Intro to Julia and the tutorial + cheatsheet He will also be teaching a tutorial session on Monday the 11th of September from 5pm-7pm in 32-155.

Grading (Tentative)

Homework: 40% (roughly 6 assignments)

Midterm project: 20%

Final project: 40%

The midterm project will be based on a video of your choosing from JuliaCon 2017, JuliaCon 2016 or JuliaCon 2015. An ideal project will explain the material and take it one or two steps further.

The final project will be of your own choosing, but must involve Julia.