Credits: 5

Schedule: 31.10.2018 - 15.02.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

II - III  2018-2019 (autumn - spring), II-III 2019-2020 (autumn - spring)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

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.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Examination, Assignments and group works

Workload (valid 01.08.2018-31.07.2020): 

Contact hrs 26 h
Independent work 84 h

Study Material (valid 01.08.2018-31.07.2020): 

Lecture handouts/slides,

Prerequisites (valid 01.08.2018-31.07.2020): 

Recommended but not obligatory: 3) Skilled in programming.

Grading Scale (valid 01.08.2018-31.07.2020): 


Registration for Courses (valid 01.08.2018-31.07.2020): 


Further Information (valid 01.08.2018-31.07.2020): 

Language class 3: English


Registration and further information