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Optimization and Machine Learning
Last Updated: 2026-06-03 00:14:17
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
- Candidates are permitted to bring one A4 sheet of notes, double-sided. The content of the sheet may be handwritten or printed.
Course Components
| Type | Title | Time & Place | Hours |
|---|---|---|---|
| lecture | Optimization and Machine Learning |
|
2 h weekly |
| exercise |
Optimization and Machine Learning
Please note that an exercise session will take place on Friday, 8 May 2026, from 10:15 to 12:00 at HG E 5.
|
|
2 h weekly |
Offered In
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Design, Mechanics and Manufacturing (Focus Coordinator: Prof. Dennis Kochmann To achieve the required 20 credit points for the Focus Specialization Design, Mechanics and Manufacturing, all of the courses listed can be selected. If required, one course from another focus specialization or from the electives of the ME Bachelor program can be selected. For recommended courses and further information, please visit the MAVT website for Focus Specialization ( ).)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
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Core Courses (The Core Courses in the Master’s program Mechanical Engineering listed below are indicative and include courses designed by the Department at the Master's level. With the approval of the tutor, students may also select Master's-level courses offered by other departments at ETH. These courses will be marked as non-regular in the LAG, but their categorization as Core Courses is possible if included in the approved LAG.)
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Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed. All courses listed as core courses (not electives) for one of the following ETH MSc programmes, MSc Statistics, MSc Physics, MSc Computer Science, MSc (Applied) Mathematics, MSc Neural Systems and Computation, MSc Robotics, Systems, and Control, MSc Data Science, MSc Electrical Engineering and Information Technology, can be taken as an elective course in the MSc CSE without prior permission.)
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Electives (These courses are particularly recommended, other ETH-courses from the field of Energy Science and Technology at large may be chosen in accordance with your tutor.)
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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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