Sustainable Business Development (neu ab WiSe 24/25)

Das neue, europaweit einzigartige Master-Studium: trinational, interdisziplinär und praxisorientiert.
Bewerben
Fakultät M+V

Technologie 3: Data Science

Empfohlene Vorkenntnisse

Technologie I und II
Lehrform Vorlesung
Lernziele / Kompetenzen

Students are familiar with the concepts of data science, machine learning, big data and artificial intelligence and their basic methods. After completing the module, students will be able to carry out data analysis from pre-processing the data to evaluating the results using machine learning or artificial intelligence methods. Students can explain their solutions and evaluate the results. Students can assess potential problems in all steps of data analysis and select suitable solutions. They are familiar with the theoretical principles and practical application of the methods. They can clearly differentiate between methods such as regression, classification, pattern recognition, machine learning and deep learning. Students are able to select, apply and, if necessary, adapt suitable methods for given problems. The main advantages and disadvantages of the methods and procedures are evaluated on a problem-specific basis.
Dauer 1
SWS 4.0
Aufwand
Lehrveranstaltung 90
Selbststudium / Gruppenarbeit: 90
Workload 180
ECTS 6.0
Voraussetzungen für die Vergabe von LP

Klausurarbeit, 90 Min.

 
Modulverantwortlicher

Prof. Dr. Manuel Lämmle
Empf. Semester 3
Haeufigkeit jedes Jahr (WS)
Verwendbarkeit

Masterstudiengang SBD
Veranstaltungen

Data Engineering and Machine Learning

Art Vorlesung
Nr. M+V2049
SWS 2.0
Lerninhalt
  • Data Engineering terminology and concepts
  • Process models for Data Engineering
  • Exploratory Data Analysis
  • Descriptive Statistics
  • Linear Regression
Literatur
  • Reddy, T. Agami, Applied data analysis and modeling for energy engineers and scientists; Springer Science & Business Media, 2011
  • Witten, I. H. and Hall, M. A., Data mining: Practical machine learning tools and techniques, 3rd ed.
  • Burlington, MA: Morgan Kaufmann, 2011
  • Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd ed. Burlington: Elsevier Science, 2011
  • Hastie, T., Tibshirani, R., and Friedman, J. H., The elements of statistical learning: Data mining, inference, and prediction, 2nd ed. Springer series in statistics. New York: Springer, 2009
  • Alpayd#n, E., Maschinelles Lernen. München: Oldenbourg, 2008.

Big Data and Artificial Intelligence

Art Vorlesung
Nr. M+V2050
SWS 2.0
Lerninhalt
  • Supvervised and Unsupervised Learning
  • Clustering and Classification Methods (Decision Trees, Random Forest, K-nearest neighbours, Naive Bayes, ...)
  • Deep Learning and artificial intelligence (Artificial Neural Networks, Multi-Layer Perceptrons, Generative AI)
  • Model Evaluation and Comparison
Literatur
  • Reddy, T. Agami, Applied data analysis and modeling for energy engineers and scientists; Springer Science & Business Media, 2011
  • Witten, I. H. and Hall, M. A., Data mining: Practical machine learning tools and techniques, 3rd ed.
  • Burlington, MA: Morgan Kaufmann, 2011
  • Han, J., Kamber, M., and Pei, J., Data Mining: Concepts and Techniques, 3rd ed. Burlington: Elsevier Science, 2011
  • Hastie, T., Tibshirani, R., and Friedman, J. H., The elements of statistical learning: Data mining, inference, and prediction, 2nd ed. Springer series in statistics. New York: Springer, 2009
  • Alpayd#n, E., Maschinelles Lernen. München: Oldenbourg, 2008.