Modulcode:	infMPAIDS-01a
Englische Bezeichnung:	Master Project - Data Science and Data Mining
Modulverantwortliche(r):	Prof. Dr. Matthias Renz
Turnus:	unregelmäßig (WS21/22 SS22 WS22/23 SS23 WS23/24 SS24)
Präsenzzeiten:	4PÜ
ECTS:	10
Workload:	300 Std. Projektarbeit
Dauer:	ein Semester
Modulkategorien:	MSc-Inf-Proj (MSc Inf (21)) 2F-MSc-Proj (2F-MA Inf (21)) MSc-WInf-Proj (MSc WInf (21)) Proj (MSc Inf (15))
Lehrsprache:	Englisch
Voraussetzungen:

Kurzfassung:

In small teams, students will get hands-on practice in implementing and applying Data Science methods with focus on data management, data mining, machine learning, similarity search, knowledge discovery among others. Targeted projects are oftern emedded in an interdisciplinary environment. This module is mainly addressing students that want to get practice on applying and developing methods they learned in the corresponding lectures. Ideally, students take this module as continuation of one or more former lectures in the above mentioned fields.

Lernziele:

Students will learn ...

• developing, implementing and evaluating methods in the areas data management, data mining, machine learning, similarity search, knowledge discovery among others in the field of data science.
• handling complex structured, semi-structured or non-structured real world data.
• to get experience in working in an interdisciplinary environment.

Lehrinhalte:

The actual teaching content changes according to the topic. Check the group website for details.

Weitere Voraussetzungen:

Good programming skills, preferably in Python. Knowledgable in basic data structures and algorithms (search, sort, ...). Prior knowledge in methods of one or more of the following topics: data mining, machine learning, similarity search, knowledge discovery, ideally achieved in corresponding lectures.

Prüfungsleistung:

Presentations including demonstration, report and the completed software system (incl. documentation).

Lehr- und Lernmethoden:

Project work.

Verwendbarkeit:

Literatur:

Verweise:

https://www.ai.informatik.uni-kiel.de/ai/teaching

Kommentar:

Available seats are very limited, depending on the topics provided in a term. Asking for the availability of avaiable seats is mandatory before registration. For asking for available seats and other related questions, please write an e-mail to ag-renz-lehre@lists.uni-kiel.de