This collection of notebooks was started for a lecture on machine learning at the Universitat Autònoma de Barcelona. It has since grown into a large part of the statistical methods lecture (SMD) at the Physics department at TU Dortmund University. It contains some mathematical derivations and small excersises to play with.
As of now, you need to execute this notebook within the project folder since it imports some plotting functions from the ml module.
The programming code examples in this material are shared under the GnuGPLv3 license. The lecture material (e.g. jupyter notebooks) are shared under the Creative Commons Attribution-NonCommercial License: https://creativecommons.org/licenses/by-nc/4.0/legalcode.txt, so they cannot be used for commercial training / tutorials / lectures.
- Data-Preprocessing and feature selection (smd_pca.ipynb)
- Introduction to supervised machine learning (smd_ml.ipynb, part 1)
- Validation, Bias-Variance-Tradeoff, ensemble methods (smd_ml.ipynb, part 2)
- Unsupervised learning, clustering (smd_unsupervised.ipynb)
- Example on FACT Data and Boosting (smd_fact_boosting.ipynb)
- Neural Networks (smd_neural_networks.ipynb)
To make sure, all needed packages are installed in an environment for these lectures, we use
conda.
Download and install Anaconda for a large collection of packages or Miniconda for a minimal starting point.
After installing conda, run
$ conda env create -f environment.yml
This will create a new conda environment with all needed packages for these lectures
named ml.
To use this environment, run
$ conda activate ml
everytime before you start working on these lectures.
From time to time, we will update the environment.yml with new versions or
additional packages, to then update your environment, run:
$ conda env update -f environment.yml
Just run
$ jupyter notebook
this will open your default browser at the overview page, where you can select each of the notebooks.