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Vorlesungsverzeichnis >> Medizinische Fakultät (Med) >>
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Bayesian Rule Mining- Dozentinnen/Dozenten
- Prof. Dr. Oliver Amft, Dr. rer. nat. Luis Ignacio Lopera Gonzales
- Angaben
- Seminar
4 SWS, benoteter Schein, Anwesenheitspflicht, ECTS-Studium, ECTS-Credits: 5, Sprache Englisch, Examination: Final project presentation, demonstrator and final report.
Zeit und Ort: n.V.; Bemerkung zu Zeit und Ort: First meeting (24.04.2019, 17:00-18:30) and seminar held at MVC, Henkestr. 91, 1st. Floor, R 373
Vorbesprechung: 24.4.2019, 17:00 - 18:30 Uhr
- ECTS-Informationen:
- Credits: 5
- Prerequisites
- Useful knowledge:
Python, data analytics
- Contents
- Seminar description:
The field of knowledge discovery has focused on the extraction of rules created from the frequent association symbols. With the recent introduction of the Bayesian rule mining algorithms, the focus is shifting from measuring relevance based on rule frequency and moving towards the increasing belief concept. Regardless of the rule relevance metric of choice, rule mining poses the secondary challenge of finding rules’ premise and conclusion symbol-sets. In this seminar, we will investigate how to transform some of the techniques used to find frequent symbol sets, into finding symbol sets that have the increasing belief property. Specifically, we will explore the use of genetic algorithms to find rules that have increasing belief.
Learning Objectives:
Gain an overview of rule mining frameworks, tools and techniques.
Understand concepts of data processing, generating process distributions and relationships.
Analyse data relationships.
Apply Mining frameworks, and compare frequent rule mining methodologies to Bayesian rule mining methods.
Create a Bayesian data miner that uses genetic algorithms.
- Literature
- Up-to-date literature recommendations are provided during the lectures.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 10, Maximale Teilnehmerzahl: 20
www: https://www.cdh.med.fau.de/2019/02/25/seminar-bayesian-rule-mining/
Für diese Lehrveranstaltung ist eine Anmeldung erforderlich.
Die Anmeldung erfolgt von Dienstag, 2.4.2019 bis Donnerstag, 18.4.2019 über: StudOn.
- Institution: Lehrstuhl für Digital Health
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