Inhalt

[ 536MLPEMSTU19 ] UE (*)Machine Learning: Supervised Techniques

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
1,5 ECTS B2 - Bachelor 2. Jahr Artificial Intelligence Arturs Berzins 1 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Bachelorstudium Artificial Intelligence 2024W
Ziele (*) This practical course complements the lecture "Machine Learning: Supervised Techniques" and aims at practicing the concepts and methods acquired in the lecture.
Lehrinhalte (*)
  • Basics of classification and regression
  • Evaluation of machine learning results (confusion matrices, ROC)
  • Under- and overfitting / bias and variance
  • Cross-validation and hyperparameter selection
  • Logistic regression
  • Support vector machines and kernels
  • Neural networks and deep networks
  • Time series (sequence) analysis
  • Bagging and boosting
  • Feature selection and feature construction
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
Frühere Varianten Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
875BIMLMSTU16: UE Machine Learning: Supervised Techniques (2016W-2019S)
675MLDAMSTU13: UE Machine Learning: Supervised Techniques (2013W-2016S)
Präsenzlehrveranstaltung
Teilungsziffer 35
Zuteilungsverfahren Direktzuteilung