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#41068 / #1

Seit WiSe 2022/23
(Deaktivierung beantragt zum WiSe 2022/23)


Machine Learning and Inverse Problems in Neuroimaging


Haufe, Stefan




Fakultät IV

Institut für Softwaretechnik und Theoretische Informatik

34352700 FG S-Professur Unsicherheit, inverse Modellierung und maschinelles Lernen

No information


MAR 4-4

Haufe, Stefan


PORD-Nr.ModultitelLPBenotungPrüfungsformPNr. (POS)Modulprüfung PORDModulprüfung PNr.

Learning Outcomes

Knowledge: Students will understand the basic concepts of statistical and physical forward and inverse problems. They will be familiar with the physics of magneto- and encephalography (M/EEG) measurements and the steps to calculate M/EEG forward models. They will know different mathematical frameworks for inverse modeling, such as dipole fitting, beamforming, distributed imaging, blind source separation, and Bayesian modeling. They will be familiar with the concept of regularization and ways to encode prior knowledge into inverse solvers. They will learn to address technical challenges such as estimating the noise level/choosing the regularization parameter. Skills: Students will be capable of modeling inverse problems as unsupervised or supervised machine learning problems. Students will also acquire or refine skills to independently review and systematically structure the literature of a well circumscribed field in order to address a given set of questions, and will gain experience in presenting the outcome to a critical audience as well as in participating in scientific discussions. Competencies: Students will be able to discuss the advantages and disadvantages of different modeling approaches depending on the problem setting and to make informed modeling decisions.


- common inverse problems in biomedicine, in particular neuroimaging - physical foundations of magneto-/electroencephalography (M/EEG) - forward modeling and physics simulation for M/EEG - dipole fits, beamforming, scanning techniques - penalized likelihood approach: smoothness, structured sparsity, elastic net, total variation denoising - Bayesian inference: maximum a-posteriori estimation, hierarchical and empirical Bayes, sparse Bayesian learning - noise learning approaches - blind source separation as a statistical inverse problem - simulations and validation - research software - applications in brain-computer interfacing and neurology

Module Components


All Courses are mandatory.

Course NameTypeNumberCycleLanguageSWSVZ
Machine Learning and Inverse Problems in NeuroimagingSEMWSEnglish2

Workload and Credit Points

Machine Learning and Inverse Problems in Neuroimaging (SEM):

Workload descriptionMultiplierHoursTotal
90.0h(~3 LP)
Pre/post processing15.04.0h60.0h
The Workload of the module sums up to 90.0 Hours. Therefore the module contains 3 Credits.

Description of Teaching and Learning Methods

The module consists of a single seminar. Students will prepare a presentation to a specific topic based on a provided collection of published material. Student presentations will be framed by short lecture segments introducing, contextualizing and connecting the presented topics. Each course slot will contain discussion periods, in which active participation is fostered. To this end, students will also work out a set of preparatory questions for each topic, as well as a brief summary.

Requirements for participation and examination

Desirable prerequisites for participation in the courses:

A BSc degree in Computer Science, Biomedical Engineering or a comparable field is recommended. Basis knowledge of linear algebra, stochastics, and numerical optimization is advantageous.

Mandatory requirements for the module test application:

No information

Module completion



Type of exam

Portfolio examination

Type of portfolio examination

100 points in total



Test elements

(Deliverable assessment) Presentation50oral30 min
(Deliverable assessment) Topic summaries50written7-12 pages

Grading scale

Test description (Module completion)

The module grade is calculated based on 1. The quality of a scientific presentation (50%). 2. The quality of short summaries for each topic (50%).

Duration of the Module

The following number of semesters is estimated for taking and completing the module:
1 Semester.

This module may be commenced in the following semesters:

Maximum Number of Participants

The maximum capacity of students is 25.

Registration Procedures

Students can sign up for the course in MOSES.

Recommended reading, Lecture notes

Lecture notes

Availability:  unavailable


Electronical lecture notes

Availability:  unavailable



Recommended literature
No recommended literature given

Assigned Degree Programs

This module is used in the following Degree Programs (new System):

Studiengang / StuPOStuPOsVerwendungenErste VerwendungLetzte Verwendung
Computer Engineering (M. Sc.)14WiSe 2022/23WiSe 2022/23
Computer Science (Informatik) (M. Sc.)14WiSe 2022/23WiSe 2022/23
Elektrotechnik (M. Sc.)12WiSe 2022/23WiSe 2022/23


No information