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Statistical Learning with Python

How can machine learning help you to find interesting patterns in large datasets, generate scientific knowledge, automate complex tasks, and assist organizations in making better decisions?

This course provides an overview of key ideas and m in supervised learning from a statistical perspective. Starting from the essentials of decision theory, model evaluation, and model selection, the course will discuss basic methods such as linear regression and logistic regression, progress to regularized estimation, nonparametric regression, additive models, and decision trees, and conclude with advanced methods such as random forests, gradient boosting, support vector machines, and neural networks.     

Find full course description in the course catalogue.    



The grade for the course consists of two parts:

  • Coursework counting app. 40 %
  • 3-hour written exam counting app. 60 %

The written exam is online and presence in Aarhus is not nescessary.

Admission Requirements

Course specific:


A Bachelor's degree in Economics and Business Administration or a related degree  


Exchange Students: nomination from your home university

Freemovers: documentation for English Language proficiency

You can read more about the admission here.


Marcel Scharth



Marcel Scharth is a lecturer in Business Analytics at the University of Sydney Business School, Australia, where he specialises in the fields of statistics, econometrics, machine learning, and data science.