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Maschinelles Lernen für Data Science

The course provides an overview of machine learning methods and algorithms for different learning tasks, namely supervised, unsupervised and reinforcement learning.

In the first part of the course, for each task the main algorithms and techniques will be covered including experimentation and evaluation aspects.

(19330101)

TypVL/Ü
Dozent/inEirini Ntoutsi, Manuel Heurich
InstitutionAG Künstliche Intelligenz und Maschinelles Lernen
Institute of Computer Science
Fachbereich Mathematik und Informatik
SemesterWintersemester 21/22
Veranstaltungsumfang
Beginn27.09.2021
Ende17.02.2022
Zeit
  • Vorlesung: jeweils mittwochs (16-18 Uhr, Hörsaal Takustr. 9)  und donnerstags (12-14 Uhr, Hörsaal Arnimallee 3)
  • Übung:  jeweils dienstags (12-14 Uhr und 14-16 Uhr), Takustr. 9, SR005.

In the second part of the course, we will focus on specific learning challenges including high-dimensionality, non-stationarity, label-scarcity and class-imbalance.

By the end of the course, you will have learned how to build machine learning models for different problems, how to properly evaluate their performance and how to tackle specific learning challenges.