Knowledge Discovery in Databases I (WS 22/23)
- Aktuelles
- Beschreibung
Organization:
Assistants: Philipp Jahn, Simon Rauch and Janina Sontheim
Tutor: Melina Bregenzer
For questions please contact the assistants Philipp and Simon.
Lecture: The lecture will be held in English. Each week’s lecture contains a topic. Corresponding slides will be published under “Material”.
The lecture will be held both in person and broadcast through Zoom: https://lmu-munich.zoom.us/j/98257032755?pwd=MzROYk1NODFRYit1RXYvd0o0NGhCQT09Exercises: Each week an exercise sheet about the previous week’s topic will be published. We encourage you to solve it on your own even though you are not required to submit it. Specially marked exercises will be corrected without bonus points. A week later the solution will be presented by our tutors.
The online tutorials on Thursdays will be held through Zoom: https://lmu-munich.zoom.us/j/92815156753?pwd=K0dRMlB1WTlWMFI2T0hHUWhuMExXdz09We offer two on-site and two online tutorials. Please carefully check the LMU page on Corona information
https://www.lmu.de/de/die-lmu/informationen-zum-corona-virus/hinweise-zu-studium-und-lehre/index.html for this!
Please note that changes may still occur and will be published via this page on Uni2Work.Discord: There is a Discord server available for the lecture where we can talk about the exercise sheets or other materials from the lecture in more depth. The invitation link is https://discord.gg/7mtusqUquj.
Please don’t send spam or ads and be respectful with each other.Content:
The vast increase in data volume in almost every field results in increased difficulty or even impossibility for information analysis. Especially in areas such as biological measurement evaluation (e.g. gene sequencing, micro-array processes …) or data transaction in large telecommunications or network operators, using data without computational aid is inconceivable. The research area “Knowledge Discovery in Databases (KDD)” investigates solutions to these problems. It combines statistics, machine learning, database systems, and (semi-) automatic extraction methods for valid, new, and potentially useful knowledge from large databases. The term data mining in this context refers to the fundamental step in the KDD process, in which the actual analysis of the data is carried out. Data mining is often applied to large amounts of operational data that are managed separately in so-called data warehouses. The frequently used term Business Intelligence describes, among other things, the application of data mining algorithms to the information provided by a data warehouse in order to support targeted decision-making processes. The lecture gives an overview of the basics of the most important KDD techniques. Particularly: Classification, regression/trend detection, clustering, outlier detection, association rules, and process mining. To deepen the lecture, exercises are offered in which the presented procedures are further explained and illustrated with practical examples.- Institut
- Institut für Informatik
- Dozent:in
- Assistent:innen
- Tutor:innen
- Korrektor:in
- Externe Homepage
- https://www.dbs.ifi.lmu.de/cms/studium_lehre/lehre_master/kdd2223/index.html
- Kursteilnehmer:innen
- 494
- Anmeldung
Do 25 Aug 2022 00:00 – Fr 31 Mär 2023 23:59
Abmeldung nur bis Fr 31 Mär 2023 23:59
- Material
Das Kursmaterial ist nur für Mitglieder des Kurses einsehbar, also z.B. für Teilnehmer:innen, Tutor:innen, Korrektor:innen und Verwalter:innen.
- Prüfungen
- Tutorien