Event
Predictive Data Analytics - An Introduction to Machine Learning [SS222500013]
Lecturers
Organisation
- KIT-Fakultät für Wirtschaftswissenschaften
Part of
- Brick Seminar in Statistics A (Master) | Industrial Engineering and Management (M.Sc.)
- Brick Seminar in Economics A (Master) | Industrial Engineering and Management (M.Sc.)
- Brick Seminar in Economics (Bachelor) | Industrial Engineering and Management (B.Sc.)
- Brick Seminar in Economics B (Master) | Industrial Engineering and Management (M.Sc.)
- Brick Seminar in Statistics B (Master) | Industrial Engineering and Management (M.Sc.)
- Brick Seminar in Statistics (Bachelor) | Industrial Engineering and Management (B.Sc.)
- Brick Seminar in Statistics A (Master) | Economics Engineering (M.Sc.)
- Brick Seminar in Economics A (Master) | Economics Engineering (M.Sc.)
- Brick Seminar in Economics (Bachelor) | Economics Engineering (B.Sc.)
- Brick Seminar in Economics B (Master) | Economics Engineering (M.Sc.)
- Brick Seminar in Statistics B (Master) | Economics Engineering (M.Sc.)
- Brick Seminar in Statistics (Bachelor) | Economics Engineering (B.Sc.)
- Brick Seminar in Statistics A (Master) | Information Systems (M.Sc.)
- Brick Seminar in Economics A (Master) | Information Systems (M.Sc.)
- Brick Seminar in Economics (Bachelor) | Information Systems (B.Sc.)
- Brick Seminar in Statistics (Bachelor) | Information Systems (B.Sc.)
- Brick Seminar in Statistics A (Master) | Information Engineering and Management (M.Sc.)
- Brick Seminar in Economics A (Master) | Information Engineering and Management (M.Sc.)
- Brick Seminar in Economics (Bachelor) | Information Engineering and Management (B.Sc.)
- Brick Seminar in Statistics (Bachelor) | Information Engineering and Management (B.Sc.)
- Brick Seminar in Statistics A (Master) | Economathematics (M.Sc.)
- Brick Seminar in Economics A (Master) | Economathematics (M.Sc.)
- Brick Seminar in Economics B (Master) | Economathematics (M.Sc.)
- Brick Seminar in Statistics B (Master) | Economathematics (M.Sc.)
Note
Modern methods from artificial intelligence and machine learning, in particular deep learning methods based on multi-layered artificial neural networks, provide unprecedented tools for data analysis and prediction. Over the past years, they have transformed many scientific fields and have become ubiquitous in real-world applications from speech recognition to self-driving cars.
This seminar will provide a broad introduction to machine learning from statistical foundations to applications in the sciences, economics and engineering. The focus will be on modern machine learning methods for predictive data analytics 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.