Machine Learning for Applied Research (CE&EE)

TitelZeitOrtDozent
Machine Learning for Applied Research (CE&EE)25.11.2025 09:00 - 16:00 (Di)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 (Mi)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 (Do)Boltzmannstr. 15; room 2101/ 2.OG (5501.02.101)Zoe Mbikayi
Keywords: 
Machine learning, data science, python, data visualization
Beschreibung Kursinhalt: 

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.

Lernziele: 

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.

Lehrmethoden: 

Presentations, hands-on exercises, and interactive discussions

Der Kurs ist für Promovierende in folgender Phase geeignet: 

Beginning of/ Halfway through the doctorate

Teilnahmevoraussetzungen: 

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

Technische Voraussetzungen: 

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

Kursvorbereitung: 

None

Sonstige Informationen: 

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

Kategorie: 
Fachspezifische Veranstaltung
Art der Veranstaltung: 
Seminar/Workshop
Veranstalter: 
Graduate Center of Computation, Information and Technology
Verantwortung für Veranstaltung: 
Hauptverantwortung
Durchführung/Format: 
In Präsenz
Sprache: 
EN
Maximale Teilnehmendenzahl: 
12
Minimale Teilnehmendenzahl: 
5
Umfang in Stunden: 
18
Kosten: 
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/).