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Topics in Algorithmic Data Analysis TADA

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