Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

Understanding of good practices for machine learning with noisy and inaccurate data; feature extraction/ feature subset selection, handling high dimensional data, ANN + Deep Learning, Probabilistic graphical models, Topic models; as well as Unsupervised learning and clustering, Anomaly detection and Recommender systems.

Credits: 5

Schedule: 13.01.2021 - 16.04.2021

Teacher in charge (valid 01.08.2020-31.07.2022): Stephan Sigg

Teacher in charge (applies in this implementation): Stephan Sigg

Contact information for the course (applies in this implementation):

CEFR level (applies in this implementation):

Language of instruction and studies (valid 01.08.2020-31.07.2022):

Teaching language: English

Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Assessment Methods and Criteria
  • Valid 01.08.2020-31.07.2022:

    Examination, Assignments and group works

Workload
  • Valid 01.08.2020-31.07.2022:

    Contact hrs 26 h
    Independent work 84 h

DETAILS

Study Material
  • Valid 01.08.2020-31.07.2022:

    Lecture handouts/slides,

Prerequisites
  • Valid 01.08.2020-31.07.2022:

    Recommended but not obligatory: 3) Skilled in programming.

SDG: Sustainable Development Goals

    4 Quality Education

    9 Industry, Innovation and Infrastructure

FURTHER INFORMATION

Description

Registration and further information