VVZ API is not affiliated with ETH Zurich. Data might be outdated or incorrect. Please view the official ETHZ Vorlesungsverzeichnis for binding information.

151-0840-00L 4 Credits BSC , DR , MSC D-MATH , D-MAVT

You're viewing possible stale or outdated data. Please check the latest semester for more up-to-date information.

Optimization and Machine Learning

Lecturers & Examiners: Dr. Bekim Berisha, Prof. Dr. Dirk Mohr

Note: previous course title until FS20 "Principles of FEM-Based Optimization and Robustness Analysis".

VVZ CR n/a

Last Updated: 2026-02-05 15:55:05

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	Fri 08:15-10:00 (ML H 44)	2 h weekly
exercise	Optimization and Machine Learning If required, two dates for exercises will be offered. Bei Bedarf werden zwei Übungstermine angeboten.	Fri 10:15-12:00 (ML F 39)	2 h weekly

Offered In

Mechanical Engineering Bachelor
- 6. Semester
  - Focus Specialization
    - Manufacturing Science (Focus Coordinator: Prof. Konrad Wegener To achieve the required 20 credit points for the focus specialization you need to pass all 3 compulsory courses (HS/FS). The other 8 credit points can be achieved from the elective courses.)
Computational Science and Engineering Bachelor
- For All Programme Regulations
  - Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
Mechanical Engineering Master
- Core Courses
  - Mechanics, Materials, Structures (The courses listed in this category “Core Courses” are recommended. Alternative courses can be chosen in agreement with the tutor.)
Computational Science and Engineering Master
- Electives (In the ‘electives’ subcategory, at least two course units must be successfully completed.)
Doctoral Department of Mechanical and Process Engineering (More Information at: )
- Doctoral and Post-Doctoral Courses