Lecturer: Prof. Dr.-Ing. Fabian Jirasek
                     Dr.-Ing. Nicolas Hayer

Workload: 2 hpw (3 or 5 ECTS as modul)

Prerequisites: Thermodynamics I, Higher Mathematics I-III

Starting date: Tue., 14^th April 2026

Language: The lecture will be held in english.

Office hours:

Office hours for the lecture and tutorial are available by appointment. Please contact Max Wagner directly via email.

Exam:

Oral exam slots are scheduled by appointment.

Course content:

In this course, we will cover the basics of machine learning and its applications in process engineering. We will also reinforce these methods through hands-on programming tasks in small groups. This lecture is designed as an introduction to machine learning. No prior knowledge in this field is required.

• Data preprocessing and representation
• Unsupervised learning
  • Dimensionality reduction
  • Clustering
• Supervised learning
  • Classification
  • Regression
• Kernel-Methods
• Probabilistic methods
• Hybrid methods
  • Ensemble methods
  • Physics-informed learning
• Training and Model Selection
  • Cross-Validation
  • Regularization

E-Learning:

All downloads and further information will be provided in the OLAT-course. Please enroll using the course code mentioned.