Further Education

What’s It About?

This seminar introduces methods from the field of Data Science and Supervised Machine Learning, as well as the statistical foundations needed to understand these methods. The seminar gives a broad overview of the subject area without using Data Science software such as R or Python. The methods, examples and tasks taught are treated theoretically and are independent of the software used later. 
 

Content

• Introduction of general terms (e.g. loss function, risk minimization, overfitting, hyper and model parameters, training and test data).
• Explanation of the operation of selected simple supervised machine learning algorithms for regression and classification
• Overview of important evaluation measures for assessing predictive performance and their properties
• Model evaluation by resampling methods  (e.g. cross validation)
• Hyperparameter tuning
  
  

Prerequisites

• General knowledge and experience in data analysis