Module Number

ML-4310 		Module Title

Data Mining and Probabilistic Reasoning 		Lecture Type(s)

Lecture, Tutorial
ECTS 3
Work load
- Contact time
- Self study
Workload:
90 h
Class time:
30 h / 2 SWS
Self study:
60 h
Duration 1 Semester
Frequency In the winter semester
Language of instruction English
Type of Exam

Written exam (in case of a small number of participants: oral tests)
Content

The lecture gives an introduction into the basics of probability theory, statistics,
information theory, data (pre-)processing and indexing techniques, graph
representations and link analysis, classification, clustering and topic models,
probabilistic inference in graphical models.
Objectives

(1) The students acquire extensive knowledge in theory and application of
methods from the field of data science.
(2) The students acquire various data science techniques for conceptual thinking,
problem formalization and problem solving.
(3) The students are introduced to challenging research questions from the field
of data science.
Allocation of credits / grading
Type of Class
Status
SWS
Credits
Type of Exam
Exam duration
Evaluation
Calculation
of Module (%)
Lecture
V
o
1
2.0
wt
90
g
100
Tutorial
Ü
o
1
1.0
Prerequisite for participation There are no specific prerequisites.
Lecturer / Other Kasneci G
Literature

Will be supplied (book chapters and papers in English)
Last offered Wintersemester 2022
Planned for ---
Assigned Study Areas INFO-INFO, INFO-PRAK, MEDI-APPL, MEDI-INFO, ML-CS, ML-DIV