Exercise: Database Systems (infDB-01a) (080025)- Dozentinnen/Dozenten
- Prof. Dr. Peer Kröger, Dr. Daniyal Kazempour, Dr. Maximilian Hünemörder, Christian Beth, M.Sc.
- Angaben
- Übung, 1 SWS
Praesenzveranstaltung
Zeit und Ort: Mo, Fr 13:00 - 14:00, 14:00 - 15:00, LMS2 - R.Ü1; Mo 15:00 - 16:00, LMS2 - R.Ü1; Di 10:00 - 11:00, 11:00 - 12:00, LMS2 - R.Ü2/K; Mi 14:00 - 15:00, 15:00 - 16:00, HRS9 - R.EG.006; Do 9:00 - 10:00, LMS8 - R.EG.017 (40)
vom 9.4.2023 bis zum 9.7.2023
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 30
- Zugeordnet zu: infDB-01a: Database Systems (080024)
inf-DSProj-01a: Data Science Projekt (inf-DSProj-01a) (080270)- Dozentinnen/Dozenten
- Prof. Dr. Peer Kröger, Dr. Maximilian Hünemörder, Mirjam Bayer, B.Eng., Marco Landt-Hayen, Dr. Rukiye Altin
- Angaben
- Seminar, 4 SWS, ECTS-Studium, ECTS-Credits: 6
Praesenzveranstaltung
Zeit und Ort: Blockveranstaltung 21.8.2023-25.8.2023 Mo-Fr, Blockveranstaltung 11.9.2023-15.9.2023 Mo, Di-Do, Fr 9:00 - 18:00, LMS8 - R.EG.007 (40); Blockveranstaltung 21.8.2023-15.9.2023 Mo-Fr 9:00 - 18:00, LMS8 - R.01.010 (24), LMS8 - R.01.011 (28)
- Voraussetzungen / Organisatorisches
- Gewünschte aber nicht verpflichtend für die Teilnahme: SoftwareEntwicklungs Course
- Inhalt
- Moodle
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 20
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 14:00 - 16:00, LMS2 - R.Ü2/K; Mi 10:00 - 12:00, LMS2 - R.Ü2/K
vom 9.4.2023 bis zum 9.7.2023
1. Prüfungstermin (Klausur am Ende der Vorlesungszeit eines Semesters): 18.7.2023, 10:00 - 12:00 Uhr, Raum CAP3 - Hörsaal 2
2. Prüfungstermin (Klausur zu Beginn der Vorlesungszeit des Folgesemesters): 19.10.2023, 9:00 - 11:00 Uhr
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. Maximilian Hünemörder, Dr. Daniyal Kazempour, Andreas Lohrer, M.Sc.
Zeit und Ort: Mi 12:00 - 14:00, LMS2 - R.Ü2/K; Bemerkung zu Zeit und Ort: Weitere Informationen folgen
infDB-01a: Database Systems (infDB-01a) (080024)- Dozentinnen/Dozenten
- Prof. Dr. Peer Kröger, Dr. Daniyal Kazempour
- Angaben
- Vorlesung, 3 SWS, ECTS-Studium, ECTS-Credits: 5
Praesenzveranstaltung
Zeit und Ort: Di, jede 2. Woche Do 14:15 - 15:45, OHP5 - [Chemie II]
vom 9.4.2023 bis zum 9.7.2023
1. Prüfungstermin (Klausur am Ende der Vorlesungszeit eines Semesters): 10.7.2023, 13:00 - 15:30 Uhr, Raum CAP2 - Hörsaal H; 10.7.2023, 13:00 - 15:30 Uhr, Raum CAP2 - Frederik-Paulsen-Hörsaal
2. Prüfungstermin (Klausur zu Beginn der Vorlesungszeit des Folgesemesters): 10.10.2023, 16:00 - 18:30 Uhr, Raum CAP2 - Frederik-Paulsen-Hörsaal; 10.10.2023, 16:00 - 18:30 Uhr, Raum CAP2 - Hörsaal H
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 333
- Zugeordnete Lehrveranstaltungen
- UE: Exercise: Database Systems (080025)
-
Dozentinnen/Dozenten: Prof. Dr. Peer Kröger, Dr. Daniyal Kazempour, Dr. Maximilian Hünemörder, Christian Beth, M.Sc.
Zeit und Ort: Mo, Fr 13:00 - 14:00, 14:00 - 15:00, LMS2 - R.Ü1; Mo 15:00 - 16:00, LMS2 - R.Ü1; Di 10:00 - 11:00, 11:00 - 12:00, LMS2 - R.Ü2/K; Mi 14:00 - 15:00, 15:00 - 16:00, HRS9 - R.EG.006; Do 9:00 - 10:00, LMS8 - R.EG.017 (40)
infMPMaL-01a: Master project - Machine Learning (infMPMaL-01a) (080269)- Dozentinnen/Dozenten
- Prof. Dr. Peer Kröger, Mirjam Bayer, B.Eng., Marco Landt-Hayen
- 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
www: https://www.isdm.informatik.uni-kiel.de/de
infSemMaLea-01a: Master Seminar - Machine Learning (infSemMaLea-01a) (080330)- Dozentinnen/Dozenten
- Prof. Dr. Peer Kröger, Dr. Daniyal Kazempour, Claudius Anton Zelenka, Dipl.-Ing.
- Angaben
- Seminar, 2 SWS, ECTS-Studium, ECTS-Credits: 5
Praesenzveranstaltung
Ort: Online-Veranstaltung
- Inhalt
- In this seminar, we will discuss recent publications in the area of Machine Learning (ML). You will review papers concerning key concepts of different state-of-the-art (SOTA) methods from different ML topics and compare different approaches. At the end of the course you will present your findings and report them in a short paper.
Your papers will all be taken from the following list of topics that are relevant to our current research:
- T1: Machine Learning for (Group) Anomaly Detection
- T2: Uncertainty in Machine Learning
- T3: Machine Learning for NLP and Geo-Temporal Analysis
To participate please write an email from your official university email (for example stuXXXXX@mail.uni-kiel.de) to alo@informatik.uni-kiel.de with the subject "Application Machine Learning Seminar" containing the following information:
- Your name, student-mail-address and Matrikelnummer
- A short application (max. 3 sentences why you are interested in joining)
- The topic that you would be interested in the most
Please submit your applications until 13.04.2022. Since we only have a limited capacity we might have to select participants and will inform you asap after the deadline if you are in.
- Zusätzliche Informationen
- Erwartete Teilnehmerzahl: 12
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