280390 VO MA PE 01 VO Inverse Problems (NPI) (2021W)
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MIXED
Registration/Deregistration
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. 10 participants
Language: English
Examination dates
Lecturers
Classes (iCal) - next class is marked with N
The course will start from the 2nd week of semester, i.e. from October 14, due to organizational reasons.
Thursday
07.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
14.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
21.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
28.10.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
04.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
11.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
18.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
25.11.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
02.12.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
09.12.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
16.12.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
13.01.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
20.01.
12:00 - 14:30
Seminarraum Paläontologie 2B311 3.OG UZA II
Thursday
27.01.
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: Mo 26.09.2022 13:10
-- Linear regression (and statistical aspects of least squares)
-- Discretization of inverse problems
-- Ill-posed problems and rank deficiency
-- Tikhonov regularization
-- Nonlinear regression (Newton method etc).
-- Bayesian approachAll of these topics will be complemented with exercises (Matlab or Python).