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Optimization and Machine Learning
Last Updated: 2026-02-05 16:23:03
Abstract
The course teaches the basics of nonlinear optimization and concepts of machine learning. An introduction to the finite element method allows an extension of the application area to real engineering problems such as structural optimization and modeling of material behavior on different length scales.
Objective
Students will learn mathematical optimization methods including gradient based and gradient free methods as well as established algorithms in the context of machine learning to solve real engineering problems, which are generally non-linear in nature. Strategies to ensure efficient training of machine learning models based on large data sets define another teaching goal of the course. Optimization tools (MATLAB, LS-Opt, Python) and the finite element program ABAQUS are presented to solve both general and real engineering problems.
Content
- Introduction into Nonlinear Optimization - Design of Experiments DoE - Introduction into Nonlinear Finite Element Analysis - Optimization based on Meta Modeling Techniques - Shape and Topology Optimization - Robustness and Sensitivity Analysis - Fundamentals of Machine Learning - Generalized methods for regression and classification, Neural Networks, Support Vector machines - Supervised and unsupervised learning
Resources
Lecture Notes
Lecture slides and literature
General Information
- Language
- English
- Levels
- BSC , DR , MSC
- Frequency
- Yearly recurring
Examination
- Type
- session examination
- Mode
- written 120 minutes
- Aids
- 1x A4 sheet, double-sided with notes/summary, scientific calculator.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Optimization and Machine Learning |
|
2 h weekly |
| exercise |
Optimization and Machine Learning
If required, two dates for exercises will be offered.
Bei Bedarf werden zwei Übungstermine angeboten.
|
|
2 h weekly |
Offered In
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Manufacturing Science (Focus Coordinator: Prof. Konrad Wegener To achieve the required 20 credit points for the focus specialization you need to pass at least 2 core courses (W+ in HS/FS). The other 12 credit points can be achieved from the elective courses (in HS/FS).)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Mechanics, Materials, Structures (The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor.)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Doctorate Mechanical and Process Engineering (More Information at: )
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Doctorate Materials Science (Further information at: )
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