teaching

  • 2021/2022: Teaching Assistant - Interpretable Machine Learning - Leibniz University Hannover
    Topics include:
    • GAMs and Rule-based Approaches
    • Feature Effects
    • Local Explanations
    • Shapley Values for Explainability
    • Instance-wise Feature Selection
    • Gradient-based Feature Attribution
    • Actionable Explanation and Resources
    • Evaluating Interpretability and Utility

  • 2019/2020/2021: Teaching Assistant - Deep Learning - Leibniz University Hannover
    Topics include:
    • Machine Learning Basics
    • Neural Net Basics
    • Convolutional Neural Nets
    • Sequence Models
    • Optimization and Regularization
    • Unsupervised Approaches: PCA, Autoencoders, GANs
    • Attention Mechanism
    • Deep Learning for Graphs
    • Deep Learning for Language
    • Interpretable Deep Learning

  • 2019: Teaching Assistant - Data Mining I - Leibniz University Hannover

  • 2017: Teaching Assistant - Hardware Lab - Leibniz University Hannover