Course material and website for the Julia programming for Machine Learning course (JuML) at the TU Berlin Machine Learning group.
Follow the installation instructions on the course website.
The course is taught in five weekly sessions of three hours. In each session, two lectures are taught:
| Lecture | Content |
|---|---|
| 0 | General Information, Installation & Getting Help |
| 1 | Basics 1: Types, Control-flow & Multiple Dispatch |
| 2 | Basics 2: Arrays & Linear Algebra |
| 3 | Plotting & DataFrames |
| 4 | Basics 3: Data structures and custom types |
| 5 | Classical Machine Learning |
| 6 | Automatic Differentiation |
| 7 | Deep Learning |
| 8 | Workflows: Scripts, Experiments & Packages |
| 9 | Testing, Profiling & Debugging |
The lectures are accompanied by four homework notebooks. The following packages are covered by the lectures and homework:
| 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 | Forwards- & 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 | 8 | Package template |
| DrWatson.jl | 8 | Workflow for scientific projects |
| Debugger.jl | 9 | Debugger |
| Infiltrator.jl | 9 | Debugger |
| ProfileView.jl | 9 | Profiler |
| Cthulhu.jl | 9 | Type inference debugger |