Exercise: Database Systems (infDB-01a) (080054)- Dozentinnen/Dozenten
- Prof. Dr. Peer Kröger, Dr. Daniyal Kazempour, Annika Huch
- Angaben
- Übung, 1 SWS
Praesenzveranstaltung
Zeit und Ort: Mo 13:00 - 14:00, CAP4 - R.13.1304 a; Mo, Mi 14:00 - 15:00, WSP3 - Seminarraum 2 (32); Di 10:00 - 11:00, 11:00 - 12:00, LMS8 - R.EG.010 (26); Mi 15:00 - 16:00, WSP3 - Seminarraum 2 (32); Do 9:00 - 10:00, LMS8 - R.EG.017 (40)
vom 14.4.2024 bis zum 14.7.2024
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 30
- Zugeordnet zu: infDB-01a: Database Systems (080051)
inf-DSProj-01a: Data Science Projekt (inf-DSProj-01a) (080270)- Dozentinnen/Dozenten
- Prof. Dr. Peer Kröger, Mirjam Bayer, M.Sc., Dr. Rukiye Altin
- Angaben
- Praktikum, 4 SWS, ECTS-Studium, ECTS-Credits: 6
Praesenzveranstaltung
Zeit und Ort: Blockveranstaltung 19.8.2024-23.8.2024 Mo-Fr 9:00 - 16:00, LMS8 - R.EG.007 (40); Blockveranstaltung 19.8.2024-13.9.2024 Mo-Fr 9:00 - 16:00, LMS8 - R.01.010 (24), LMS8 - R.01.011 (28); Einzeltermin am 13.9.2024 9:00 - 16:00, LMS8 - R.EG.007 (40)
- Voraussetzungen / Organisatorisches
- Gewünschte aber nicht verpflichtend für die Teilnahme: SoftwareEntwicklungs Course
- Inhalt
- Moodle
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 40
infADMMLM-01a: Advanced Data Mining and Machine Learning Methods (infADMMLM-01a) (080098)- Dozent/in
- Prof. Dr. Peer Kröger
- Angaben
- Vorlesung, 4 SWS, ECTS-Studium, ECTS-Credits: 8
Praesenzveranstaltung, Unterrichtssprache Englisch
Zeit und Ort: Mo 14:15 - 15:45, LMS8 - R.EG.009 (24); Mi 10:15 - 11:45, LMS8 - R.EG.009 (24)
vom 14.4.2024 bis zum 14.7.2024
1. Prüfungstermin (Klausur am Ende der Vorlesungszeit eines Semesters): 16.7.2024, 14:00 - 16:00 Uhr, Raum LMS6 - R.11
Bemerkung zu Zeit und Ort: Weitere Informationen folgen
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 20
- Zugeordnete Lehrveranstaltungen
- UE: Exercise: Advanced Data Mining and Machine Learning Methods (080099)
-
Dozentinnen/Dozenten: Dr. Daniyal Kazempour, Andreas Lohrer, M.Sc.
Zeit und Ort: Mi 12:15 - 13:45, LMS8 - R.EG.009 (24); Bemerkung zu Zeit und Ort: Weitere Informationen folgen
infDB-01a: Database Systems (infDB-01a) (080051)- Dozent/in
- Prof. Dr. Peer Kröger
- Angaben
- Vorlesung, 3 SWS, ECTS-Studium, ECTS-Credits: 5
Praesenzveranstaltung
Zeit und Ort: Di 16:15 - 17:45, CAP3 - Hörsaal 3; Blockveranstaltung 2.5.2024-11.7.2024 Do 14:15 - 15:45, OHP5 - [Chemie II] (außer Do 23.5.2024, Do 6.6.2024, Do 20.6.2024, Do 4.7.2024)
vom 14.4.2024 bis zum 14.7.2024
1. Prüfungstermin (Klausur am Ende der Vorlesungszeit eines Semesters): 15.7.2024, 12:00 - 14:00 Uhr, Raum LS1 - Klaus-Murmann-Hörsaal; 15.7.2024, 12:00 - 14:00 Uhr, Raum CAP3 - Hörsaal 2; 15.7.2024, 12:00 - 14:00 Uhr, Raum CAP2 - Hörsaal C; 15.7.2024, 12:00 - 14:00 Uhr, Raum CAP3 - Hörsaal 3; 15.7.2024, 12:00 - 14:00 Uhr, Raum LMS6 - R.10 Steinitz-Hörsaal; 15.7.2024, 12:00 - 14:00 Uhr, Raum CAP2 - Frederik-Paulsen-Hörsaal
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 250
- Zugeordnete Lehrveranstaltungen
- UE: Betreutes Arbeiten der Pflichtveranstaltungen im Bachelor Informatik und Wirtschaftsinformatik (080291)
-
Dozentinnen/Dozenten: Dr. Pamela Fleischmann, Dr.-Ing. Claudius Zelenka, David Fischer, Annika Huch, Dr. Gregor Große-Bölting, N.N.
Zeit und Ort: Fr 14:00 - 17:00, LMS8 - R.EG.016 (40) (außer Fr 19.4.2024, Fr 28.6.2024); Fr 14:00 - 17:00, LMS8 - R.EG.017 (40) (außer Fr 19.4.2024); Fr 14:00 - 17:00, LMS8 - R.EG.009 (24), LMS8 - R.EG.010 (26); Fr 14:00 - 17:00, LMS8 - R.EG.015 (40) (außer Fr 28.6.2024); Fr 15:00 - 17:00, LMS8 - R.01.010 (24); Fr 14:00 - 17:00, LMS8 - R.01.019 (40); Einzeltermine am 19.4.2024, 28.6.2024 14:00 - 17:00, Raum n.V.
- UE: Exercise: Database Systems (080054)
-
Dozentinnen/Dozenten: Prof. Dr. Peer Kröger, Dr. Daniyal Kazempour, Annika Huch
Zeit und Ort: Mo 13:00 - 14:00, CAP4 - R.13.1304 a; Mo, Mi 14:00 - 15:00, WSP3 - Seminarraum 2 (32); Di 10:00 - 11:00, 11:00 - 12:00, LMS8 - R.EG.010 (26); Mi 15:00 - 16:00, WSP3 - Seminarraum 2 (32); Do 9:00 - 10:00, LMS8 - R.EG.017 (40)
Master Project Module (080293)- Dozentinnen/Dozenten
- Dr. Daniyal Kazempour, Sweety Mohanty, Prof. Dr. Peer Kröger
- Angaben
- Übung, 4 SWS, ECTS-Studium, ECTS-Credits: 10
Praesenzveranstaltung, Unterrichtssprache Englisch
Ort: Online-Veranstaltung
- Inhalt
- Master Project: Machine Learning
In today's digitalized life, the amount of generated data reaches new dimensions year by year. The enormous value gained from analysing this data plays an essential role for numerous operations of various domains, such as Health, Manufacturing, Mobility and so on. Consequently, the discovery process to gain such insights is becoming increasingly critical for everyone aiming at a career in the job area or research field of Data Science, Big Data and Advanced Analytics or Machine Learning.
In this practical course, you will learn how to approach (real life) questions and tasks in the greater area of Machine Learning and Big Data applications. You will work in small groups of three to five students while being guided to coordinate yourself and divide your workload, aiming to give you a small insight of how project work can be handled as well as deepen your skills in communication and time management. By working with state-of-the-art ML tools and libraries you will broaden your knowledge and get hands-on experience of many phases included in an actual Machine Learning process, starting from raw data, up to the extraction and interpretation of patterns and their visualization as knowledge and insights.
The topics this year include (note, these might still be subject to change):
Reinforcement Learning in Boardgames (How can I teach a Machine to win a game?)
Incorporating Knowledge Graphs to build and optimize a food recommendation system (How can we tell which food is healthy and good?)
Timeseries prediction with ANNs on non-linear and non-stationary timeseries (How could we predict stock price movements?)
Natural disaster control using image data (Can we use satellite images to identify wildfires?)
"What's between 'em" - On the discovery of inter-class instances (What should we do with data points in between multiple clusters?)
Quantum Machine Learning (QML) for Anomaly Detection (AD) (How can QML embeddings optimize the performance and explainability for Unsupervised and Semisupervised AD?)
and more
Requirements are a basic familiarity with Python or the commitment to pick up on that, very basic knowledge in Machine Learning and Big Data (i.e. you have attended the lectures "Knowledge Discovery and Data Mining", "Big Data Management and Analytics" or similar) and an open mind to work as a team!
If you are interested to join, please send us an e-mail to miba@informatik.uni-kiel.de with the Subject "Master Project 2023". Please also include a short informal statement which topics you are most interested in and why - what background knowledge (i.e. lectures, libraries or tools) you already have - and, if you are applying in groups, with whom you would like to work as a team.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 15
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