280390 VO MA PE 01 VO Inverse Problems (NPI) (2019W)
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Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).
Details
max. 20 participants
Language: English
Examination dates
Lecturers
Classes (iCal) - next class is marked with N
Thursday
03.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
10.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
17.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
24.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
31.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
07.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
14.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
21.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
28.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
05.12.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
12.12.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
09.01.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
16.01.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
23.01.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
30.01.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Information
Aims, contents and method of the course
Assessment and permitted materials
oral examination
Minimum requirements and assessment criteria
Examination topics
Reading list
R.C. Aster, B. Borchers, C.H. Thurber: Parameter Estimation and Inverse Problems, Elsevier, 2013
Association in the course directory
Last modified: Sa 02.04.2022 00:25
-- Linear regression (and statistical aspects of least squares)
-- Discretization of inverse problems (mainly integrals)
-- Ill posed problems and rank deficiency
-- Tikhonov regularization
-- Nonlinear regression (Newton method,...).
-- Bayesian approachAll these chapter will be complemented with exercises (Matlab or Python).