Machine Learning and Decision Making Summer 2020

General Remarks

Because of travel restrictions associated with the corona pandemic, Prof. Mukhopadhyay cannot come to Ulm to teach this course in the summer term 2020. But it will be offered this year in the winter term. Sessions will take place in November 2020.

Characterizing the course

It is important to differentiate between anecdotal evidence and evidence that can be tested and proved. Understanding and analyzing data is essential for the latter. All of us in day-to-day routine use numbers in our calculations. Problems in business contain a great degree of quantitative element in the form of facts and figures. It is essential for professionals to carry out data analysis and interpretation for effective decisions. In this context, they need to prepare quantitative arguments to justify their decisions. Decision making using machine learning techniques is the answer for accomplishing this purpose. The course aims to develop your analytical skills - both the ability to conceptualize a problem as well as solve them broadly, it aims to make you understand and appreciate the most widely used tools of machine learning  which form the basis for rational and sound  decisions

  • Focus on problem recognition and test hypothesis/model in the context of managerial decision-making.
  • Develop skills in analysis and interpretation of data
  • Handle challenging problems using appropriate analysis tools

Course Content

-   Introduction to analytics using machine learning

-   Introduction to R Studio and QGIS

-   Unsupervised learning

-   Supervised learning: Continuous response and classification

-   Boosting and bagging

-   Model comparisons


  • Gareth, J., D. Witten, T. Hastie and R. Tibshirani, “An Introduction to Statistical Learning with Applications in R”, Springer series.
  • Additional references will be provided in class.


Please see "General remarks" to the left.

Dates and Room

Please see "General remarks" to the left.


Grading is based on a paper that you submit according to instructions given at the start of the course. There will be no written exam.


Module description

This lecture earns you 4 credit points and it is open for

  • Wiwi (BSc, MSc, Dipl)
  • WiMa/WiPhy (BSc, MSc, Dipl)
  • Finance (MSc)

and others according to study plans.