Machine Learning for Applied Research (CE&EE)

TitleTimeRoomTeacher
Machine Learning for Applied Research (CE&EE)25.11.2025 09:00 - 16:00 (Tue)Boltzmannstr. 15; room 2101/ 2.OG (5501.02.101)Zoe Mbikayi
Machine Learning for Applied Research (CE&EE)26.11.2025 09:00 - 16:00 (Wed)Boltzmannstr. 15; room 2101/ 2.OG (5501.02.101)Zoe Mbikayi
Machine Learning for Applied Research (CE&EE)27.11.2025 09:00 - 16:00 (Thu)Boltzmannstr. 15; room 2101/ 2.OG (5501.02.101)Zoe Mbikayi
Keywords: 
Machine learning, data science, python, data visualization
Course Description: 

This workshop introduces participants to the practical application of machine learning in engineering and applied research. Covering supervised learning, model training, validation and deployment, it equips participants with the skills to design effective ML pipelines and understand real-world challenges such as overfitting.

Course aims: 

Day 1:

• Framing problems

• Supervised learning and model training

Day 2:

• Metrics, cross-validation, hyperparameter tuning

• Machine Learning Pipelines

Day 3:

• Application challenges, overfitting, limitations

• Model deployment

Participants will learn how to apply machine learning in engineering.

Teaching methods: 

Presentations, hands-on exercises, and interactive discussions

This course fits doctoral candidates in the following phase: 

Beginning of/ Halfway through the doctorate

Participation requirements: 

This course is intended as a subject-specific course only for doctoral candidates of CE&EE. Other doctoral candidates of the CIT can later enter it as a transferable skills course

Solid Python knowledge

Technical requirements: 

Each participant should have a laptop with at least 16 Gb ram.

Course preparation: 

None

Additional information: 

You need to take part in at least 80% of the course to have it approved for your qualification program

Category: 
Fachspezifische Veranstaltung
Event type: 
Seminar/Workshop
Organizer: 
Graduate Center of Computation, Information and Technology
Responsibility for event: 
Hauptverantwortung
Format: 
In Präsenz
Course Language: 
EN
Course Capacity (Max): 
12
Course capacity (Min): 
5
Duration in hours: 
18
Financial contribution: 
free of charge
Trainer: 
Zoe Mbikayi, M.Sc.

Research Associate at the Institute of Flight System Dynamics at the University of Munich. Zoe is experienced in programming, machine learning and workshop training. More information about him can be found on his profile page (https://zmbikayi.github.io/).