This class delves deeper into quantitative academic research methods. Specifically, this course will focus on machine learning methods and provide an introduction into machine learning. This course will cover the following content: 

• What is machine learning?
• Assessing model accuracy und bias-variance trade-off
• Regression and classification problems
• Resampling methods, regularization and linear model selection
• Tree-based methods (regression/classification trees, random forests, boosting, bagging) 

The course assumes that students have already a basic understanding of linear regression models. Coding experience in Python or R is a plus.

After completion of this course, students have a basic understanding of how machine learning methods can be applied in a supply chain management and business analytics context. Students will gain a detailed understanding of selected quantitative research methods and be able to implement them computationally, as well as applying them in relevant problem settings.

According to the examination regulation full attendance is necessary.

Lecture, scientific computing, classroom discussions, assignments, cases.

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