Chair of Artificial Intelligence and Machine Learning

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Thesis Proposals

Below you will find a number of thesis topic proposals for Bachelor or Master's theses. Contact Viktor Bengs or Marcel Wever for individual thesis topics.

(Most recently added topics first.)

  • Can I trust my Explainable AI algorithm? Evaluating and Benchmarking xAI algorithms. (Ba/Ma) (PDF 190KB)
  • Label-wise Baselearner Configuration in Classifier Chains for Multi-Label Classification (Ba/Ma) (PDF 99KB)
  • Efficient Optimization of Hierarchical Multi-Label Classifier Ensembles (Ba/Ma) (PDF 146KB)
  • On the Importance of Hyperparameter Optimization in Automated Machine Learning (Ba/Ma) (PDF 98KB)
  • Transfer Learning for Automated Machine Learning (Ba/Ma) (PDF 99KB)
  • Fuzzy Pattern Trees as Deep Fuzzy Systems (Ba/Ma) (PDF 229KB)
  • Learning from Imprecise Data with an Adjusted Infimum Loss (Ba/Ma) (PDF 230KB)
  • Conformal Rule-Based Multi-label Classification (Ba/Ma) (PDF 156KB)
  • Deep Aggregation Autoencoders (Ma) (PDF 81KB)
  • Self-training based on superset learning (Ba/Ma) (PDF 107KB)
  • Learning to aggregate the assessment of arguments in computational argumentation (Ba/Ma) (PDF 85KB)
  • A systematic review of list-wise learning to rank (Ba) (PDF 80KB)
  • Mixed Dyad Ranking (Ba/Ma) (PDF 82KB)
  • Calibration of scoring classifiers: Survey and empirical comparison (Ba/Ma) (PDF 86KB)
  • Sampling nested dichotomies for multi-class classification (Ba/Ma) (PDF 84KB)
  • Rank Aggregation for Incomplete Rankings (Ba/Ma) (PDF 92KB)
  • Solving label ranking problems via error-correcting output codes (Ba/Ma) (PDF 83KB)
  • Data-driven adaptation of weighted rank correlation measures (Ba/Ma) (PDF 94KB)