Module Data Science, Computer Science (Master) (ER 8)

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Module summary

Data Science

INFM120ML

Prof. Dr. Reimar Hofmann

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All semesters

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At the end of this module, students will have understood the theoretical and mathematical foundations of machine learning and data analysis. They will be able to assess the suitability of different methods for specific situations, interpret phenomena they observe conclusively and, if necessary, derive ideas for improving the selected approaches. 


The skills taught in the module are advantageous for participation in the Artificial Intelligence module.

Written/verbal Exam 120/20 Min. (graded)
Course Data Science

INFM121ML.a

Lecture

Prof. Dr. Reimar Hofmann

German

2/2

60 hours in total, including 30 hours of contact study.

Module exam

  • Scale types (nominal, ordinal, interval, ratio), conversion of scale types (one-hot-coding etc.), standardization
  • Explorative data analysis, differentiation between direct and indirect dependencies, data visualization
  • Statistical principles of machine learning, maximum likelihood approach, bias and variance (overfitting) as sources of learning error 
  • Cost functions for numerical regression and classification
  • Criteria foe data quality, dealing with quality deficiencies (e.g. missing values, outliers)
  • Dealing with more complex data types (record data, heterogeneous data, bag of words), data transformations, feature engineering
  • Dimension reduction: heuristic, manual, etc.
  • Volume reduction: sampling etc.

Course Optimization

INFM121ML.b

Lecture

Prof. Dr.-Ing. Astrid Laubenheimer

German

2/2

60 hours in total, including 30 hours of contact study.

Module exam

Course Optimization Exercise

INFM122ML

Exercise

Prof. Dr.-Ing. Astrid Laubenheimer

German

3/2

90 hours in total, including 30 hours of contact study.

Hands-on Work 1 Semester (graded)