Inhalt

[ 536DASCMLTK24 ] KV (*)Machine Learning: Basic Techniques

Versionsauswahl
(*) Leider ist diese Information in Deutsch nicht verfügbar.
Workload Ausbildungslevel Studienfachbereich VerantwortlicheR Semesterstunden Anbietende Uni
3 ECTS B2 - Bachelor 2. Jahr Artificial Intelligence Sepp Hochreiter 2 SSt Johannes Kepler Universität Linz
Detailinformationen
Quellcurriculum Bachelorstudium Artificial Intelligence 2024W
Ziele (*)Data analysis and visualization are essential to most fields in science and engineering. The goal of this course is to provide students with a basic tool chest of methods for pre-processing, analyzing, and visualizing scientific data.
Lehrinhalte (*)
  • Scatter plots and box plots
  • Basics of classification
  • Basics of regression
  • ANOVA
  • Clustering
  • Principal component analysis (+ visualization, including spectral maps)
  • Singular value decomposition (+ visualization)
  • Matrix factorization (+ visualization)
  • Independent component analysis
Beurteilungskriterien (*)The final grade is based on a combined assessment of homework and a final exam.
Lehrmethoden (*)Slide presentations complemented by online demos and examples presented on the blackboard; demos are based on the scientific computing platform R
Abhaltungssprache Englisch
Literatur (*)Electronic course material and example code are made available for download
Lehrinhalte wechselnd? Nein
Frühere Varianten Decken ebenfalls die Anforderungen des Curriculums ab (von - bis)
536DASCBMDAK19: KV Basic Methods of Data Analysis (2019W-2024S)
875BIMLMDAK16: KV Basic Methods of Data Analysis (2016W-2019S)
675MLDAMDAK13: KV Basic Methods of Data Analysis (2013W-2016S)
Präsenzlehrveranstaltung
Teilungsziffer 35
Zuteilungsverfahren Direktzuteilung