A growing collection of resources about Machine Learning eXplainability. Our objective is to cover both theory and practice, including:
- high-level overviews & introductory examples;
- mathematical foundations;
- algorithmic implementations;
- practical advice & real-life caveats; and
- success & failure case studies.
We will be adding more eXplainable Machine Learning techniques to our compendium over time, so stay tuned. Cannot wait? Contribute a technique description yourself.
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_xml-book-page _xml-book-page Public archive
Hosting of the XML Book – the content is deployed from the xml-book repository.
Jupyter Notebook
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usi-slides-mirror usi-slides-mirror Public archive
USI XML slides -- mirror (without a custom domain)
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Repositories
- _xml-book-page Public archive
Hosting of the XML Book – the content is deployed from the xml-book repository.
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