The module Advanced Data Analytics for Management Support (ADAMS) introduces students to the latest developments in the scope of data-driven management support. It covers relevant theories and concepts in machine learning against the background of concrete real-world applications in management science. Special emphasize is given to the analysis of textual data and other complex data such as sequences or images. Corresponding data is typically approached using the framework of deep artificial neural networks. The module recognizes the importance of deep learning and elaborates on corresponding methodologies. Frameworks and practices to use advanced (deep) machine learning technology and deploy corresponding solutions are of critical importance.
Topics covered in the module include but are not limited to:
- Fundamentals of artificial neural networks
- Recurrent networks for sequential data processing with applications in finance
- Convolutional neural networks
- Generative models and adversarial learning
- Fundamentals of textual data analysis
- Neural network-based text embeddings: word2vec and cousins
- Approaches for topic modeling and sentiment analysis
- A primer in reinforcement learning
The module draws on the concepts and practices covered in Business Analytics & Data Science (BADS). Successful completion of BADS is a prerequisite to take this module.
The module makes use of the Python programming language. Fundamentals of machine learning in Python will be covered in the first weeks of the tutorial sessions. However, students must be prepared to invest a sizeable about of time into self-study to internalize relevant programming skills and gain the experience needed for subsequent tutorials. The grading of the module will be based on a practical assignment, which also involves Python programming. |
