Workshop: Practical Anomaly Detection

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Target Audience

Entry to mid-level data scientists / machine learning engineers / ...

Goals

This workshop introduces the participants to the topic of anomaly detection. Inparticular, it provides initial answers for the following questions:

1. What is an anomaly?
2. Where can anomaly detection be applied?
3. What methods are available?
4. How can I evaluate the performance of an anomaly detection system?
5. What is Extreme Value Theory (EVT)?
6. How EVT contributes to anomaly detection?

Successful participants will acquire the basic theoretical knowledge and the practical skills to perform anomaly detection on simple use cases using state-of-the-art methods. While mostly covering introductory material, the workshop tries to provide enough depth to be interesting for participants with some experience in anomaly detection.

Prerequisites

We try to keep the prerequisites as low as possible. Some experience with standard machine learning methods and basic knowledge of the python machine learning stack are however highly recommended.

Setup

Besides setting up the environment yourself, we provide a devcontainer that can either be used locally or inside a GitHub Codespace. To quickly spin up an instance, holding the training's content and the necessary environment, click the green button "Code" in the top right corner of the repository and select "Codespaces" rather than local development.

If you prefer to work locally, you can set up the environment as follows:

We recommend to install rise with conda (installation with pip may cause problems). We also use the spellchecker and equation-numbering extensions.

To configure everything, activate a conda env and run

conda install -c conda-forge notebook rise jupyter_contrib_nbextensions jupyter_nbextensions_configurator
conda install -c conda-forge ffmpeg
python ./configure_spellcheck_dict.py
jupyter nbextension enable spellchecker/main
jupyter nbextension enable equation-numbering/main

Use the extension-configurator for customizing your slideshow as described here.

The hide_code extension is useful to see the slides in presentation mode. You can install it by typing the following lines

pip install hide_code
jupyter nbextension install --py hide_code
jupyter nbextension enable --py hide_code
jupyter serverextension enable --py hide_code

Finally, clone repository, change into the directory of the cloned repository and type

pip install -e .

License

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.