151-0840-00L  Optimization and Machine Learning

SemesterSpring Semester 2021
LecturersB. Berisha, D. Mohr
Periodicityyearly recurring course
Language of instructionEnglish
CommentNote: previous course title until FS20 "Principles of FEM-Based Optimization and Robustness Analysis".


151-0840-00 VOptimization and Machine Learning2 hrs
Fri08:15-10:00ML H 44 »
B. Berisha, D. Mohr
151-0840-00 UOptimization and Machine Learning
If required, two dates for exercises will be offered.

Bei Bedarf werden zwei Übungstermine angeboten.
2 hrs
Fri10:15-12:00ML F 39 »
B. Berisha, D. Mohr

Catalogue data

AbstractThe 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.
ObjectiveStudents 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
Lecture notesLecture slides and literature

Performance assessment

Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course
ECTS credits4 credits
ExaminersB. Berisha, D. Mohr
Typesession examination
Language of examinationEnglish
RepetitionThe performance assessment is offered every session. Repetition possible without re-enrolling for the course unit.
Mode of examinationwritten 120 minutes
Written aids1x A4 sheet, double-sided with notes/summary, scientific calculator.
This information can be updated until the beginning of the semester; information on the examination timetable is binding.

Learning materials

No public learning materials available.
Only public learning materials are listed.


No information on groups available.


There are no additional restrictions for the registration.

Offered in

Doctoral Department of Mechanical and Process EngineeringDoctoral and Post-Doctoral CoursesWInformation
Mechanical Engineering BachelorManufacturing ScienceW+Information
Mechanical Engineering MasterMechanics, Materials, StructuresWInformation
Computational Science and Engineering BachelorElectivesWInformation
Computational Science and Engineering MasterElectivesWInformation