Session 0 :: Warm-up
Order of the day

  • 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

Who am I ? (Daniel)
  • 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)
Who am I ? (Paul)
  • Artist and Programmer
  • Wissenschaftlicher Mitarbeiter von Daniel
  • BA in Visuelle Kommunikation
  • plsdlr.net
Who am I ? (Nik)
  • Artist and Electrical Engineer
  • Studentische Hilfskraft von Daniel
  • BA in Electrical Engineering / KuM Absolvent(Almost)
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
Session 0 :: Warm-up
Session 1 :: 15th May :: Features 1
Session 2 :: 12th June :: Classifiers






12.6.2020 / AE500612 :: Neighbors & Classifiers
Session 3 :: 26th June :: Evaluation & Tradeoffs
Session 4 :: 19th June :: Tradeoff
Session 5 :: 3rd July :: Integration & Deployment
What is this course NOT about ?

it is not about neural networks *


it is not about "regression"


 


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

What is this course about ?

about learning


about classification


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

← →

/