Modal Symbolic Learning: A Tutorial – Material

This repository contains the slides and the Jupyter notebooks for "Modal Symbolic Learning: A Tutorial" (online video lectures here).

You can inspect the notebooks without cloning the repo; simply click on any ipynb file above.

Otherwise... Clone the repo, open a Julia session:

git clone https://github.com/aclai-lab/modal-symbolic-learning-course/
cd modal-symbolic-learning-course
julia -t8 # With 8 threads, for example

Then, activate and instantiate the environment:

Pkg.activate(".")
Pkg.instantiate()

This will install all dependencies, including:

• IJulia.jl: Julia backend for Jupyter Notebook;
• MLJ.jl: general-purpose machine learning framework;
• Sole.jl: framework for (modal) symbolic learning;
• ModalDecisionTrees.jl: machine learning algorithm for extracting modal decision trees.

To open the notebooks, start Jupyter Notebook with the Julia backend by typing:

using IJulia; IJulia.notebook(; dir=".")

Have fun! 😄

Online references

• Documentation for MLJ: https://alan-turing-institute.github.io/MLJ.jl
• Documentation for Sole: https://aclai-lab.github.io/Sole.jl/
• Documentation for SoleLogics: https://aclai-lab.github.io/SoleLogics.jl/
• Documentation for SoleModels: https://aclai-lab.github.io/SoleModels.jl/
• Documentation for ModalDecisionTrees: https://aclai-lab.github.io/ModalDecisionTrees.jl/