Bachelor seminar KMD

every

(in German: Bachelorseminar KMD )

Module-ID: FIN-INF-999924

Link:	LSF
Responsibility:	Myra Spiliopoulou
Lecturer:	Myra Spiliopoulou
Classes:	Seminar KMD_BachelorSeminar
Applicability in curriculum:	- B.Sc. INF: Informatik - Wahlpflicht - B.Sc. INF: Schlüssel- und Methodenkompetenzen - B.Sc. INF: Studienprofil: Künstliche Intelligenz - B.Sc. CV: Informatik - Wahlpflicht - B.Sc. CV: Schlüssel- und Methodenkompetenzen - B.Sc. INGINF: Informatik - Wahlpflicht - B.Sc. INGINF: Schlüssel- und Methodenkompetenzen - B.Sc. WIF: Verstehen und Gestalten - Wahlpflicht - B.Sc. WIF: Gestalten und Anwenden - Wahlpflicht - B.Sc. WIF: Schlüssel- und Methodenkompetenzen - B.Sc. INF (bilingual): Informatik - Wahlpflicht - B.Sc. INF (bilingual): Schlüssel- und Methodenkompetenzen

Abbreviation KMD_BachelorSeminar

Credit Points 5

Semester every

Term

Duration 1 Semester

Language english

Level Bachelor

Intended learning outcomes:
When successfully completing this seminar, the students:

• can read and present a scientific paper
• can compare mining methods, workflows and frameworks that address a specific scientific topic, and can rank them on given criteria
• can critically discuss the advantages and caveats of a published mining method
• can summarize one or more papers , their comparisons and critical discussion in an essay
• can present the scientific paper(s) they have read in a pre-specified amount of time and in a way understandable to both an audience that has read the papers and to an audience that has not, and they can answer questions

Content:
CONTENT: Topics on 'Knowledge Management and Discovery' for business applications, engineering applications and applications in healthcare; all topics refer to mining algorithms STRUCTURE: Each student receives a topic as assignment. The assignment encompasses:

• one or two papers in the topic's area
• a series of questions to be answered from the papers

The student must:

1. read the papers
2. compare the methods and results in them in order to answer the scientific questions
3. critically discuss and rank the methods compared in the previous step
4. present the above in class and answer questions
5. write an essay on them

Workload:
28 h Präsenz + 94 h selbstständige Arbeit COURTESY TRANSLATION: 28 h in class + 94 h self-study

Pre-examination requirements:	Type of examination:	Teaching method / lecture hours per week (SWS):
	Referat	Seminar (2 SWS)

Prerequisites according to examination regulations:	Recommended prerequisites:
none	Familiarity with mining algorithms Familiarity with methods for model evaluation

Media:	Literature:
	The seminar contains several topics. The mapping of the module (see field 'Verwendbarkeit') depends on the topic assigned to a specific student.

Comments: