• 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

• 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)

• Artist and Electrical Engineer
• Studentische Hilfskraft von Daniel
• BA in Electrical Engineering / KuM Absolvent(Almost)

• Artist and Programmer
• Wissenschaftlicher Mitarbeiter von Daniel
• BA in Visuelle Kommunikation
• plsdlr.net

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

it is not about neural networks *

it is not about "regression"

* well, it will be also about neural networks, but just a little bit ;)

about learning

about classification

about main machine learning concepts: true positive / true negative / false positive / false negative / feature, feature extraction, classifier, accuracy etc.