Course on machine learning (Feb. 2020)
This course will be organized within the graduate school of the KIT center MathSEE at the Department of Mathematics (Campus South of KIT).
The course will provide a broad introduction to machine learning from mathematical foundations to applications in the sciences, economics and engineering. The focus will be on modern machine learning methods such as random forests, gradient boosting machines and neural networks, their trans-disciplinary application to supervised learning tasks, and approaches to gain insight into the 'black box' of machine learning models. Lectures on the theoretical background will be accompanied by hands-on programming exercises in Python that will cover practical aspects of implementing machine learning methods for analyzing scientific and real-world datasets.
The number of participants is limited to 25.