Please note! Course description is confirmed for two academic years (1.8.2018-31.7.2020), which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.
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.
Schedule: 01.03.2021 - 09.04.2021
Teacher in charge (valid 01.08.2020-31.07.2022): Nuutti Hyvönen
Teacher in charge (applies in this implementation): Nuutti Hyvönen
Contact information for the course (applies in this implementation):
CEFR level (applies in this implementation):
Language of instruction and studies (valid 01.08.2020-31.07.2022):
Teaching language: English
Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
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
Teaching methods: lectures, exercises and home exam.
Assessment methods: exercises, a home exam.
Contact hours 36h (no compulsory attendance)
Self-study ca 100h
All essential material is included in the lecture notes that are available at the course's homepage.
Substitutes for Courses
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.
- Teacher: Nuutti Hyvönen