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Xannadoo/README.md

Hej 👋

I'm Chrisanna, otherwise know as Sanna.

I'm studying MSc Data Science at ITU Copenhagen, with a focus on explainability of ML models.

I am interested in knowing why. More specifically, why do machine learning models make the decisions they do? Why did they return the response they did? How do we go about creating explainations and how reliable is that process?

Fun fact: The name Xannadoo comes from Coleridge's poem Kubla Khan. Many years ago, a collegue of mine misheard my name and it stuck. I also used to live in the birthplace of Coleridge, Ottery St Mary before moving to Denmark.

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  1. RoDS-2024 RoDS-2024 Public

    Reflections on Data Science 2024 exercises

    Jupyter Notebook

  2. carbonCostKaggle/carbon-cost-kaggle carbonCostKaggle/carbon-cost-kaggle Public

    Investigating the carbon cost of machine learning competitions that use medical image datasets.

    Jupyter Notebook 1

  3. Algorithmic_Fairness_Mandatories Algorithmic_Fairness_Mandatories Public

    Repo for the mandatory assignments Algorithmic Fairness Spring 2024. Group Equalised Odds

    Jupyter Notebook

  4. AML_Classifying_distracted_drivers AML_Classifying_distracted_drivers Public

    Mandatory assignment for Advanced Machine Learning, ITU Spring 2024

    Jupyter Notebook