- founder of oldest Slovak digital community kyberia.sk
- Bc. in humanities (Charles University in Prague) and Bc. in linguistics (Universite de Nice Sophia-Antipolis)
- MSc. in cognitive sciences (Ecole Pratique des Hautes Etudes, Paris)
- PhDs. in psychology (Universite Paris 8) and cybernetics (Slovak University of Technology)
- ex IT-Admin of UdK's Medienhaus
- Digital Education juniorprofessor (Einstein Center Digital Future / UdK)
In this course, we are going to follow some nice O'Reilly data science manual and, line by line, learn about meaning of terms like "feature", "multi-class classification", "training" and "cross validation" and, while doing so, acquire all necessary prerequisities of "the most sexy job of 22nd century".
We start this Friday (24th April) at 10:00 am
- 10:00 - 10:15 :: Digitales Ankommen
- Introduction
- Course overview
- Machine-learning intro
- Jupyter intro
- Warm-up exercise (in break-out rooms ??)
- Discussion
- Warm-up exercise (in break-out rooms ??)
- Discussion
- Artist and Programmer
- Wissenschaftlicher Mitarbeiter von Daniel
- BA in Visuelle Kommunikation
- plsdlr.net
- Artist and Electrical Engineer
- Studentische Hilfskraft von Daniel
- BA in Electrical Engineering / KuM Absolvent(Almost)
about learning
about classification
about main machine learning concepts: true positive / true negative / false positive / false negative / feature, feature extraction, classifier, accuracy etc.
it is not about neural networks *
it is not about "regression"
* well, it will be also about neural networks, but just a little bit ;)