Data Mining – Einführung in Data Mining (for Diploma Supplement only, not valid for study)

Winter

(engl. Data Mining for Bachelor Students - An Introduction )

Modulnummer: FIN-INF-110302

Link zum LSF:	LSF
Verantwortung:	Myra Spiliopoulou
Dozent:in:	Myra Spiliopoulou
Lehrveranstaltungen:	Courtesy translation of the description of a german course Lecture class DM4BA Exercise class DM4BA
Verwendbarkeit:	- B.Sc. INF: Informatik - Wahlpflicht - B.Sc. INF: Studienprofil: Künstliche Intelligenz - B.Sc. CV: Informatik - Wahlpflicht - B.Sc. INGINF: Informatik - Wahlpflicht - B.Sc. WIF: Verstehen und Gestalten - Wahlpflicht - B.Sc. WIF: Gestalten und Anwenden - Wahlpflicht - B.Sc. INF (bilingual): Informatik - Wahlpflicht

Kürzel DM4BA

CP 5

Semester Winter

Fachsem. None

Dauer 1 Semester

Sprache deutsch

Niveau Bachelor

Angestrebte Lernergebnisse:
DISCLAIMER: Courtesy translation of the description of a german course, not valid for study The students:

• understand what data mining is good for
• understand what a classification task looks like and what a clustering task looks like and can distinguish between the two types of tasks
• understand how simple classification methods work and can apply them to problems
• understand how simple clustering methods work and can apply them to problems
• understand the challenges of evaluating models
• can design and carry out evaluation processes
• understand why data engineering is necessary before calling learning procedures
• can apply simple data preparation tools and evaluate their results

and have thus acquired the necessary basics to design a simple process for deriving a model and evaluating it.

Inhalt:
Courtesy translation of the description of a german course

• Classification: Learning procedures and evaluation processes
• Clustering: Learning procedures and evaluation processes
• more about methods for the evaluation of models
• Data engineering: data preparation tasks, methods and evaluation processes

Arbeitsaufwand:
Courtesy translation of the description of a german course 56 h attendance (lecture and exercise classes) + 94 h independent work (for lectures, exerices and for the exam preparation)

Prüfungsvorleistungen:	Studien-/Prüfungsleistungen:	Lehrform / SWS:
Courtesy translation of the description of a german course A minimum number of points must be achieved during the course.	Courtesy translation of the description of a german course Written exam in the form of 'Klausur' (120 min)	Courtesy translation of the description of a german course Lecture (2 SWS) Exercise (2 SWS)

Voraussetzungen nach Prüfungsordnung:	Empfohlene Voraussetzungen:
none	Courtesy translation of the description of a german course Datenbanken (a German course)

Medienformen:

Literatur:

Data Mining: Pan-Ning Tan, Michael Steinbach, Anuj Karpatne, Vipin Kumar (2019). Introduction to Data Mining, 2nd edition. Pearson – Ausschnitte aus Kpt 3, 5, 7 Ian Witten & Eibe Frank (2005). Data Mining – Practical machine learning tools and techniques. 2nd edition. Elsevier – Ausschnitte aus Kpt 4 zu Naive Bayes Data Engineering: Salvador Garcia, Julian Luengo, Francisco Herrera (2015). Data Preprocessing for Data Mining. Springer – Ausschnitte aus Kpt 3, 4, 7, evtl auch aus Kpt 2 Timeseries: Parmezan, A. R. S., Souza, V. M. A., and Batista, G. E. A. P. A. (2019). Evaluation of statistical and machine learning models for time series prediction: Identifying the state-of-the-art and the best conditions for the use of each model. Information Sciences, DOI: 10.1016/j.ins.2019.01.076 Cismondi, F. C., Fialho, A., Vieira, S., Reti, S., Sousa, J., and Finkelstein, S. (2013). Missing data in medical databases: Impute, delete or classify? Artificial Intelligence in Medicine, DOI: 10.1016/j.artmed.2013.01.003 More literature appears in the individual blocks of the course.

Hinweise: