Machine learning and causal inference
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Introduction to machine learning (from a computational biology perspective)
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.. note::

  Aims:

  - get intuition and high-level understanding of what machine learning is and what types of problems it can help solving;
  - this material will also show that machine learning is not just a black box, but that different choices have important implications;
  - provide the overall understanding that there are a data representation component and a machine learning algorithm;
  - achieve high-level understanding of machine learning workflow, comparison and the related uncertainty

  Level: beginner 🌱

If you are new to machine learning and want to apply it in a biological discipline, the following material might provide a convenient introduction.
It consists of 6 parts and combines the content and references with tasks and quizzes to test the covered material.

.. toctree::
   :maxdepth: 1
   :caption: Topics:

   ml_intro/overview
   ml_intro/intro_to_ml
   ml_intro/ml_models
   ml_intro/data_representation
   ml_intro/ml_model_comparison_and_uncertainty
   ml_intro/transparency_and_reproducibility

Recommended material
----------------------

Recommended courses at UiO:

- `IN5400/IN9400 Machine Learning for Image Analysis <https://www.uio.no/studier/emner/matnat/ifi/IN5400/index-eng.html>`_ 🌿
- `STK-IN9300 Statistical Learning Methods in Data Science <https://www.uio.no/studier/emner/matnat/math/STK-IN9300/>`_ 🌿

Recommended seminars at UiO:

- `Research Seminar at the Section for Machine Learning, Department of Informatics <https://www.mn.uio.no/ifi/english/about/organisation/mls/seminar/>`_

Recommended books:

- `Deep Learning book <https://www.deeplearningbook.org/>`_ by Goodfellow et al. 2016
- `The Elements of Statistical Learning <https://web.stanford.edu/~hastie/ElemStatLearn/>`_ by Hastie et al. 2008

Relevant papers:

- `Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning <http://arxiv.org/abs/1811.12808>`_ by Raschka 2018

- `DOME: recommendations for supervised machine learning validattion in biology <https://www.nature.com/articles/s41592-021-01205-4>`_ by Walsh et al. 2021

- `An introduction to domain adaptation and transfer learning <http://arxiv.org/abs/1812.11806>`_ by Kouw and Loog 2018

- `Causality for Machine Learning <http://arxiv.org/abs/1911.10500>`_ by Schölkopf 2019

- `Causal Inference and the Data-Fusion Problem <https://www.pnas.org/content/113/27/7345>`_ by Bareinboim and Pearl 2016

- `Towards Causal Representation Learning <https://ieeexplore.ieee.org/document/9363924>`_ by Schölkopf et al. 2021