Inhalt

[ 993MLPETCMU20 ] UE (*)Theoretical Concepts of Machine Learning

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
1,5 ECTS M2 - Master 2. Jahr Informatik Bernhard Nessler 1 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Masterstudium Artificial Intelligence 2021W
Ziele (*)This practical course complements the lecture "Theoretical Concepts of Machine Learning" and aims at practicing the concepts and methods acquired in the lecture.
Lehrinhalte (*)
  • Generalization error
  • Bias-variance decomposition
  • Error models
  • Model comparisons
  • Estimation theory
  • Statistical learning theory
  • Worst-case and average bounds on the generalization error
  • Structural risk minimization
  • Bayes framework
  • Evidence framework for hyperparameter optimization
  • Optimization techniques
  • Theory of kernel methods
Beurteilungskriterien (*)Assignments during the semester plus final exam
Lehrmethoden (*)Students are given assignments in 1-2 week intervals. Homework must be handed in. Results are to be presented and discussed in the course.
Abhaltungssprache Englisch
Literatur (*)Assignments and homework submissions are managed via JKU Moodle. Where necessary, complimentary course material is provided for download.
Lehrinhalte wechselnd? Nein
Sonstige Informationen (*)Until term 2020S known as: INMAWUETCML UE Theoretical Concepts of Machine Learning
Präsenzlehrveranstaltung
Teilungsziffer 35
Zuteilungsverfahren Direktzuteilung