Applied Machine Learning
This course teaches about the functionality of different methods from the field of machine learning. In addition, their various applications as well as the challenges in their application are illustrated with real practical examples.
The main topics of the course are:
- Methods from the areas of
- Supervised Learning (e.g. linear regression, logistic regression, artificial neural networks, decision tree)
- Unsupervised Learning (especially clustering methods like K-Means, DBSCAN and hierarchical clustering)
- Reinforcement Learning (Q-Learning)
- Machine Learning process: pre-processing, data cleansing, data analysis, evaluation
- Application of machine learning to data types, e.g. time series.
In the exercises, students will learn to apply and investigate the processes in the Python programming language. The main Python libraries used in this process are numpy, pandas, matplotlib, and scikit-learn.