M.O.D.E.A #4 : (Machine) learning and data (science)
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
- 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 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)
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.
Success of Your machine learning approach depends in great measure from quality of Your features.
12.6.2020 / AE500612 :: Neighbors & Classifiers
19.6. 12:00 - 13:30 Digital
26.6. 12:00 - 13:30 Digital
10.7. 10:00 - ?? Physical (Kleistpark)
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