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README.md

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 [![PyPI](https://img.shields.io/pypi/v/slise)](https://pypi.org/project/slisemap/)
 [![Documentation](https://github.com/edahelsinki/slisemap/actions/workflows/python-docs.yml/badge.svg)](https://edahelsinki.github.io/slisemap/slisemap/)
 [![Tests](https://github.com/edahelsinki/slisemap/actions/workflows/python-pytest.yml/badge.svg)](https://github.com/edahelsinki/slisemap/actions/workflows/python-pytest.yml)
-[![License: MIT](https://img.shields.io/github/license/edahelsinki/slisemap)](https://github.com/edahelsinki/slisemap/blob/master/LICENSE)
+[![Licence: MIT](https://img.shields.io/github/license/edahelsinki/slisemap)](https://github.com/edahelsinki/slisemap/blob/master/LICENSE)
 [![Binder](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/edahelsinki/slisemap/HEAD?labpath=examples)
 
 # SLISEMAP: Combine supervised dimensionality reduction with local explanations
 
-SLISEMAP is a supervised dimensionality reduction method, that takes data, in the form of vectors, and predictions from a *black box* regression or classification model as input. SLISEMAP then simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. The explanations consists of *white box* models that locally approximate the *black box* model.
+SLISEMAP is a supervised dimensionality reduction method, that takes data, in the form of vectors, and predictions from a *black box* regression or classification model as input. SLISEMAP then simultaneously finds local explanations for all data items and builds a (typically) two-dimensional global visualisation of the black box model such that data items with similar local explanations are projected nearby. The explanations consist of *white box* models that locally approximate the *black box* model.
 
-SLISEMAP is implemented in *Python* using *PyTorch* for efficient optimisation, and optional GPU-acceleration. For more information see the [full paper](https://arxiv.org/abs/2201.04455), the [demo paper](https://github.com/edahelsinki/slisemap/blob/main/examples/demo_paper.pdf), the [demonstration video](https://youtu.be/zvcFYItwRlQ) ([slides](https://github.com/edahelsinki/slisemap/blob/main/examples/slisemap_demo_paper_v2.pdf)), the [examples](https://github.com/edahelsinki/slisemap/tree/main/examples), or the [documentation](https://edahelsinki.github.io/slisemap/slisemap).
+SLISEMAP is implemented in *Python* using *PyTorch* for efficient optimisation, and optional GPU-acceleration. For more information see the [full paper](https://arxiv.org/abs/2201.04455), the [demo paper](https://github.com/edahelsinki/slisemap/blob/main/examples/demo_paper.pdf), the [demonstration video](https://youtu.be/zvcFYItwRlQ) ([slides](https://github.com/edahelsinki/slisemap/blob/main/examples/demo_presentation.pdf)), the [examples](https://github.com/edahelsinki/slisemap/tree/main/examples), or the [documentation](https://edahelsinki.github.io/slisemap/slisemap).
 
 
 ## Citation

mkdocs.yml

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       accent: deep orange
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         name: Switch to dark mode
     - media: "(prefers-color-scheme: dark)"
       scheme: slate
@@ -45,10 +45,11 @@ nav:
     - PyPI: https://pypi.org/project/slisemap/
     - Paper: https://arxiv.org/abs/2201.04455
     - Demo Paper: https://github.com/edahelsinki/slisemap/blob/main/examples/demo_paper.pdf
+    - Presentation: https://github.com/edahelsinki/slisemap/blob/main/examples/demo_presentation.pdf
     - Examples: https://github.com/edahelsinki/slisemap/tree/main/examples
 
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