meta [readme]: Switch the readme.rst to markdown.

MANIFEST.in

-include LICENSE README.rst
+include LICENSE README.md
 recursive-include doc conf.py *.rst
 recursive-include src/bob/learn/em/data *.*

README.rst → README.md

-.. vim: set fileencoding=utf-8 :
-.. Mon 15 Aug 2016 09:48:28 CEST
 
-.. image:: https://img.shields.io/badge/docs-latest-orange.svg
-   :target: https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html
-.. image:: https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/pipeline.svg
-   :target: https://gitlab.idiap.ch/bob/bob.learn.em/commits/master
-.. image:: https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/coverage.svg
-   :target: https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/coverage
-.. image:: https://img.shields.io/badge/gitlab-project-0000c0.svg
-   :target: https://gitlab.idiap.ch/bob/bob.learn.em
+[![badge latest doc](https://img.shields.io/badge/docs-latest-orange.svg)](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/sphinx/index.html)
+[![badge pipeline](https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/pipeline.svg)](https://gitlab.idiap.ch/bob/bob.learn.em/commits/master)
+[![badge coverage](https://gitlab.idiap.ch/bob/bob.learn.em/badges/master/coverage.svg)](https://www.idiap.ch/software/bob/docs/bob/bob.learn.em/master/coverage)
+[![badge gitlab](https://img.shields.io/badge/gitlab-project-0000c0.svg)](https://gitlab.idiap.ch/bob/bob.learn.em)
 
-
-=================================================
- Expectation Maximization Machine Learning Tools
-=================================================
+# Expectation Maximization Machine Learning Tools
 
 This package is part of the signal-processing and machine learning toolbox
-Bob_. It contains routines for learning probabilistic models via Expectation
-Maximization (EM).
+[Bob](https://www.idiap.ch/software/bob). It contains routines for learning
+probabilistic models via Expectation Maximization (EM).
 
 The EM algorithm is an iterative method that estimates parameters for
 statistical models, where the model depends on unobserved latent variables. The
@@ -28,7 +19,8 @@ computes parameters maximizing the expected log-likelihood found on the E step.
 These parameter-estimates are then used to determine the distribution of the
 latent variables in the next E step.
 
-The package includes the machine definition per se and a selection of different trainers for specialized purposes:
+The package includes the machine definition per se and a selection of different
+trainers for specialized purposes:
 
  - Maximum Likelihood (ML)
  - Maximum a Posteriori (MAP)
@@ -40,23 +32,17 @@ The package includes the machine definition per se and a selection of different
  - EM Principal Component Analysis (EM-PCA)
 
 
-Installation
-------------
+## Installation
 
-Complete Bob's `installation`_ instructions. Then, to install this package,
-run::
+Complete Bob's [installation](https://www.idiap.ch/software/bob/install)
+instructions. Then, to install this package, run:
 
-  $ conda install bob.learn.em
+``` sh
+conda install bob.learn.em
+```
 
 
-Contact
--------
+## Contact
 
 For questions or reporting issues to this software package, contact our
-development `mailing list`_.
-
-
-.. Place your references here:
-.. _bob: https://www.idiap.ch/software/bob
-.. _installation: https://www.idiap.ch/software/bob/install
-.. _mailing list: https://www.idiap.ch/software/bob/discuss
+development [mailing list](https://www.idiap.ch/software/bob/discuss).

pyproject.toml

@@ -58,7 +58,7 @@
     package-dir = {"" = "src"}
 
 [tool.setuptools.dynamic]
-    readme = {file = "README.rst"}
+    readme = {file = "README.md"}
 
 
 [tool.distutils.bdist_wheel]