Credits: 5

Schedule: 25.02.2019 - 05.04.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

IV Spring (2018-2019, 2019-2020)


Learning Outcomes (valid 01.08.2018-31.07.2020): 

You will learn to identify an ill-posed inverse problem and to understand the restrictions its nature imposes on the solution process. You will familiarize yourself with several classical regularization methods for finding approximate solutions to linear ill-posed problems. You will learn to formulate an inverse problem as a Bayesian problem of statistical inference and to interpret the information contained in the resulting posterior probability distribution. You will learn to numerically implement the introduced solution techniques.


Content (valid 01.08.2018-31.07.2020): 

The course’s topic is computational methods for solving inverse problems arising from practical applications. The course consists of two parts: the first three weeks focus on classic regularization techniques, the latter three weeks discuss statistical methods.


Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Teaching methods: lectures, exercises and home exam.

Assessment methods: exercises, a home exam.


Workload (valid 01.08.2018-31.07.2020): 

Contact hours 36h (no compulsory attendance)

Self-study ca 100h


Study Material (valid 01.08.2018-31.07.2020): 

All essential material is included in the lecture notes that are available at the course's homepage.


Substitutes for Courses (valid 01.08.2018-31.07.2020): 



Course Homepage (valid 01.08.2018-31.07.2020):

Prerequisites (valid 01.08.2018-31.07.2020): 

MS-A00XX, MS-A01XX, MS-A02XX, MS-A050X. The courses MS-A030X, MS-C134X, MS-C1650, MS-E1460, MS-E1651, MS-E1652, MS-E2112 may also be useful.


Grading Scale (valid 01.08.2018-31.07.2020): 




Registration and further information