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Julia Programming for Machine Learning course at TU Berlin

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JuML Logo

Julia Programming for Machine Learning

Go to course website TU Berlin ISIS page

Course material and website for the Julia Programming for Machine Learning course (JuML) at the TU Berlin Machine Learning group.

Installation

Follow the installation instructions on the course website.

Contents

Lectures

The first half of the course is taught in five weekly sessions of three hours. In each session, two lectures are taught:

Week Lecture Content
1 0 General Information, Installation & Getting Help
1 Basics 1: Types, Control-flow & Multiple Dispatch
2 2 Basics 2: Arrays, Linear Algebra
3 Plotting & DataFrames
3 4 Basics 3: Data structures and custom types
5 Classical Machine Learning
4 6 Automatic Differentiation
7 Deep Learning
5+ Project Workflows: Scripts, Experiments & Packages
Project Profiling & Debugging

The first three weeks focus on teaching the fundamentals of the Julia programming language. These weeks consist of longer lectures, followed up by shorter, "guided tours" of the Julia ecosystem, including plotting, data-frames and classical machine learning algorithms.

Week four is all about Deep Learning: A comprehensive lecture on automatic differentiation (AD) sheds light on differences between Julia's various AD packages, before giving a brief overview of Flux's Deep Learning ecosystem.

Finally, week five is all about starting your own Julia project, taking a look at the structure of Julia packages and different workflows for reproducible machine learning research. This is followed up by a demonstration of Julia's debugging and profiling utilities.

The lectures and the homework cover the following packages:

Package Lecture Description
LinearAlgebra.jl 2 Linear algebra (standard library)
Plots.jl 3 Plotting & visualizations
DataFrames.jl 3 Working with and processing tabular data
MLJ.jl 5 Classical Machine Learning methods
ChainRules.jl 6 Forward- & reverse-rules for automatic differentiation
Zygote.jl 6 Reverse-mode automatic differentiation
Enzyme.jl 6 Forward- & reverse-mode automatic differentiation
ForwardDiff.jl 6 Forward-mode automatic differentiation
FiniteDiff.jl 6 Finite differences
FiniteDifferences.jl 6 Finite differences
Flux.jl 7 Deep Learning abstractions
MLDatasets.jl 7 Dataset loader
PkgTemplates.jl Project Package template
DrWatson.jl Project Workflow for scientific projects
Debugger.jl Project Debugger
Infiltrator.jl Project Debugger
ProfileView.jl Project Profiler
Cthulhu.jl Project Type inference debugger

Project

In the second half of the course, after passing the homework, students work in groups on a small programming project of their choice, learning best practices for package development in Julia, such as:

  • how to structure and develop a package
  • how to write package tests
  • how to write and host package documentation

During code review sessions, students give each other feedback on their projects before presenting their work in end-of-semester presentations.