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Julia for Machine Learning course at TU Berlin
| Date | Stars |
|---|---|
| 2026-07-24 | 272 |
| 2026-07-25 | 272 |
| 2026-07-28 | 272 |
| 2026-07-30 | 272 |
| 2026-07-31 | 272 |
| 2026-08-06 | 272 |
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<div align="center">
<a href="https://adrhill.github.io/julia-ml-course/">
<img
src="website/_assets/logo/logo.svg"
alt="JuML Logo"
height="100"
/>
</a>
<br />
<p>
<h1>
<b>
Julia for Machine Learning
</b>
</h1>
[![Go to course website][goto-badge]][site-url]
</p>
</div>
> [!IMPORTANT]
> The course material for the JuML course at the [TU Berlin Machine Learning group][ml-group-url] has moved to the [`JuML-TUB` organization](https://github.com/JuML-TUB/julia-ml-course).
Course material and website for the [Julia for Machine Learning][site-url] course (JuML).
## Installation
Follow the [installation instructions](https://adrhill.github.io/julia-ml-course/E1_Installation/)
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 |
| DrExcerpt of 5,124 characters
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