Applied Data Analytics and Machine Learning in Python

What it is about

You will learn how to practically apply Python and common frameworks for data analysis and for machine learning. The focus is on modelling and implementing ML solutions — especially reinforcement learning and unsupervised learning — for application-oriented use cases from control engineering, process control and quality monitoring.

What you will be able to do

• Understanding the basic principles of machine learning (especially reinforcement learning and unsupervised learning) from an application-oriented perspective
• Confidently using Python and common frameworks (e.g. TensorFlow, Keras, scikit-learn) for data analysis and model building
• Design and implementation of suitable data analysis and ML pipelines
• Reading and utilizing online documentation for open-source Python libraries
• Modeling complex control problems (e.g. flow charts, state diagrams) and implementation in Python
• Formulation and development of a control task as a reinforcement learning task
• Assessment of deployment, effort and challenges of reinforcement learning in automation-related tasks

What the module consists of

• TheorieteilConveys the necessary theoretical foundations through lectures and presentations as preparation for the internship
• Praktikum / ÜbungsblockIndependent work on exercises and use cases, implementation and documentation of solutions, group work and presentation of results

Teaching method

• Vorträge und PräsentationenConveying the theoretical foundations that are a prerequisite for the practical work
• Praktische Übungen mit ÜbungsskriptStep-by-step tackling of tasks with increasing difficulty to deepen programming and modeling skills
• Gruppenarbeit und Peer-ReviewDiscussion and presentation of the developed solutions to promote scientific communication and critical reflection
• Demo-Use CaseGoing through a complete development process from problem formulation to plant control to apply what was learned