Seminar
The TransferLab Seminar is a platform where researchers and engineers share and discuss recent advances in AI/ML, striking a balance between accessibility and mathematical depth.
We organize comprehensive talks on ML, RL, DL and statistics, emphasizing both theoretical understanding and practical implementation. These range from introductory overviews to recent developments - and are open to all interested individuals!
Each talk, lasting about 45min followed by around 15min discussion, focuses on a specific topic related to our ongoing series topics. To stay informed, follow the appliedAI Institute on LinkedIn. If you’re interested in presenting your work, contact us.
Upcoming Talks
Scheduled
Scientific Inference With Interpretable Machine Learning -
Timo Freiesleben (University of Tübingen)
Previous Talks
Done
Uncertainty quantification with conformal prediction -
Miguel de Benito Delgado
Done
Preventing bias through decision-making algorithms -
Done
Robustly representing uncertainty through sampling in deep neural networks -
Fabio Peruzzo (appliedAI Initiative)
Done
Dropout as a Bayesian Approximation: Representing Model Uncertainty in Deep Learning -
Iván Rodríguez (appliedAI Initiative)
Done
Aleatoric and epistemic uncertainty in machine learning -
Michael Panchenko (appliedAI Initiative)
Done
Monte Carlo-Dropout for Uncertainty Quantification in Deep Learning -
Maternus Herold (appliedAI Institute)