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.
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.
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
Examination, Assignments and group works
Contact hrs 26 h
Independent work 84 h
Recommended but not obligatory: 3) Skilled in programming.
SDG: Sustainable Development Goals
4 Quality Education
9 Industry, Innovation and Infrastructure