Lecture materials Monday
- Prof. Eyke Hüllermeier - Uncertainty Quantification in Machine Learning: From Aleatoric to Epistemic; and/or Preference Learning and Ranking. PDF
- Jaan Aru „The neural idea factory: How new ideas come about“ PDF
- Raul Vicente „Complexity Science for Computer Scientists“
Lecture materials Tuesday
- Prof. Eyke Hüllermeier - Uncertainty Quantification in Machine Learning: From Aleatoric to Epistemic; and/or Preference Learning and Ranking. PDF
- Marek Rei „Encoders: The Art of Packing Text into Vectors“ PDF
- Mark Fišel „Learning to Write Text (Artificially)“ PDF
- Christoph Lampert „Fair and Robust Machine Learning“ PDF
- Novin Shahroudi „Beyond Marginals On importance of methods for joint distribution modeling“ PDF
- „Using machine translation to solve Estonian NLU tasks A case study on the COPA task“ PDF
- Taavi Kivisik „Cognitive Data Structures and Algorithms“ PDF
Lecture materials Wednesday
- Christoph Lampert „Fair and Robust Machine Learning“ PDF
- Meelis Kull „Calibrated uncertainty: machine learning methods that know how well they know“ PDF
- Tanel Tammet „On Common Sense“ PDF
- Christoph Lampert „Behind the scenes: How does one become a (machine learning) researcher and what does it mean to be one?“ (Public lecture) PDF