General
- study semester
-
4-5
- standard study semester
-
6
- cycle
- every summer semester
- duration
- 1 semester
- SWS
- 2
- ECTS
- 6
- teaching language
- English
People
- responsible
-
Prof. Dr. Jilles Vreeken
- lectures
-
Prof. Dr. Jilles Vreeken
Assessment & Grades
- entrance requirements
-
a background in statistics, machine learning, and/or data mining is strongly recommended (e.g. Elements of Machine Learning, Elements of Statistical Learning, Machine Learning, or Information Retrieval and Data Mining)
- assessment / exams
oral exam and written assignments
- grade
Will be determined from performance in examinations and exercises. The exact modalities will be announced at the beginning of the module.
Workload
- course type /weekly hours
2 h lectures
= 2 h (weekly)
- total workload
30 h of classes
+ 150 h private study
= 180 h (= 6 ECTS)
Aims / Competences to be developed
- Thorough understanding of selected advanced topics in data analysis.
- Ability to quickly understand the main gist in scientific literature, without getting lost in details, critically assessing claims, seeing through the hype.
- Ability to comparatively analyse and reason about (seemingly disparate) concepts and methods, quickly developing meta-level understanding of advanced topics.
Content
During the course we consider hot topics in machine learning and data mining that are also important to understand deeply. The exact topics we will cover will differ per year, but for example often include aspects of Pattern Discovery, Dependency Discovery, Causal Inference, and Fairness.
Literature & Reading
Recent scientific publications from the top venues in machine learning and data mining.
Additional Information
Curriculum
This module is part of the following study programmes:
Visual Computing MSc: Image Related Fields
study semester: 3 / standard study semester: 4
Cybersecurity BSc (English): Komplementäre Themen der Cybersicherheit
study semester: 4-5 / standard study semester: 6