250078 VO Advanced numerical analysis (2023S)
Labels
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
Language: English
Examination dates
Wednesday
28.06.2023
Wednesday
28.06.2023
Monday
24.07.2023
Monday
11.09.2023
Friday
06.10.2023
Thursday
18.01.2024
Lecturers
Classes (iCal) - next class is marked with N
The first course on wednesday 1. march 13h15 serves as a short "organisational meeting", both for the lecture and for the exercise classes (called "introductory seminar" for unknown reasons)
The precise time of the lectures can be slightly shifted if students wish.
Wednesday
01.03.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
06.03.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
08.03.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
15.03.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
20.03.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
22.03.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
27.03.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
29.03.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
17.04.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
19.04.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
24.04.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
26.04.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
03.05.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
08.05.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
10.05.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
15.05.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
17.05.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
22.05.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
24.05.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
31.05.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
05.06.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
07.06.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
12.06.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
14.06.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
19.06.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
21.06.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Monday
26.06.
15:00 - 16:30
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
28.06.
13:15 - 14:45
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Information
Aims, contents and method of the course
Assessment and permitted materials
written or oral exam after the end of the course.
lecture notes etc can be used for the exam.
lecture notes etc can be used for the exam.
Minimum requirements and assessment criteria
Minimum (prior) requirements: Basic knowledge of numerical methods / analysis on bachelor level.Assessment criteria: Oral / written exam assessing the topics and exercises presented in the lecture.
Examination topics
Understanding of what was presented in the lecture course.
In addition, the ability to apply the presented results will be assessed using example problems and exercises will be discussed in the exam.
In addition, the ability to apply the presented results will be assessed using example problems and exercises will be discussed in the exam.
Reading list
will be presented in the first lecture
Association in the course directory
MAMN
Last modified: We 24.01.2024 14:26
In some sense it is a follow up to any elementary course on "Numerical Mathematics" in a bachelor program.
It is apt for students in master programs "mathematics", "computational sciences", "data science"Topics.
.) numerics of eigenvalue problems ,
.) iterative methods for large linear systems,
.) nonlinear systems of equations also in higher dimensions
.) introduction to numerics partial differential equations
.) "Modern methods": neural networks, machine learning.! It is strongly recommended to do also the "exercise course" 250 071 called "introductory seminar" !The exam for the lecture will consist partially of the problems of the exercise classes.