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

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