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Institute of Computational Perception
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Machine Learning and Artificial Intelligence

Student Projects in Machine Learning and Artificial Intelligence

Much of our research applies AI and Machine Learning (see further below), but we are also interested in developing, improving or understanding Deep Learning or Statistical Learning/Modeling in general: How does it work? When does it fail? What can we do when data is scarce? Projects may involve re-implementing methods from the literature, systematic experiments with designed data sets, attempts at qualitative or performance improvements, etc. These projects generally require a background in Machine Learning.

Contact (if not stated otherwise): Jan Schlüter

These student projects can be started any time (including holidays) and can span over semester boundaries.

Remark: We are open for new proposals - if you are interested in Machine Learning / Artificial Intelligence, feel free to contact us!


Interpretable Machine Learning / Explainable Artificial Intelligence (Contact: Verena Praher):

  • Concept-based Explanation Methods (e.g. TCAV, Non-negative Concept Activation Vectors, Concept Bottleneck Models, ...)
  • Evaluating Explanations
  • Debugging Models using Tools from IML / XAI
  • The User's Perspective in Explainability
  • Frameworks for IML / XAI
  • Mechanistic Interpretability
  • Counterfactual Explanations (Contact: Katharina Hoedt)

Adversarial Examples (Contact: Katharina Hoedt):

  • Adversarial Robustness
  • Reasons for Adversarial Vulnerability
  • Linking Adversarial Vulnerability and Explainability

Representation Learning:

Generative Models:

Machine Learning Theory: