250076 VO Measure and integration theory (2023W)
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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
Language: English
Examination dates
Tuesday
30.01.2024
Monday
05.02.2024
Monday
05.02.2024
08:00 - 11:15
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
16.02.2024
Friday
19.04.2024
11:30 - 14:00
Hörsaal 11 Oskar-Morgenstern-Platz 1 2.Stock
Lecturers
Classes (iCal) - next class is marked with N
Monday
02.10.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
06.10.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
09.10.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
13.10.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
16.10.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
20.10.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
23.10.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
27.10.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
30.10.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
03.11.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
06.11.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
10.11.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
13.11.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
17.11.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
20.11.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
24.11.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
27.11.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
01.12.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
04.12.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Monday
11.12.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
15.12.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
08.01.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
12.01.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
15.01.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
19.01.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
22.01.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Friday
26.01.
13:15 - 14:45
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Monday
29.01.
08:00 - 09:30
Hörsaal 3 Oskar-Morgenstern-Platz 1 Erdgeschoß
Information
Aims, contents and method of the course
The theory of measure and integration belongs to the foundations of modern analysis, and provides the formal framework for probability theory. This course introduces its central concepts and results (discussing in particular: existence, uniqueness, basic properties, and examples of measures; the abstract Lebesgue integral, convergence theorems, and spaces of integrable functions; product measures; measures with densities; applications to real analysis), and offers brief appetizers for some more advanced topics.The teaching method is by lectures, based on the textbooks. There is an exercise class associated with the course (see 250069 PS).
Assessment and permitted materials
Written exam.
Minimum requirements and assessment criteria
At least half of the total points of the written exam needs to be achieved to pass.
Exam questions include both exercises (in order to demonstrate a working knowledge of the material) and theory questions (in order to demonstrate a solid understanding of the content of the course).
Exam questions include both exercises (in order to demonstrate a working knowledge of the material) and theory questions (in order to demonstrate a solid understanding of the content of the course).
Examination topics
All the course contents, including the proofs of the main results presented during the lectures. Further information will be provided during the course.
Reading list
Richard F. Bass, Real Analysis for Graduate Students, Version 4.3 (available online).
Sheldon Axler, Measure, Integration & Real Analysis, Graduate Texts in Mathematics (available online).
Elias M. Stein and Rami Shakarchi, Real Analysis: Measure Theory, Integration, and Hilbert Spaces, Princeton Lectures in Analysis.
Sheldon Axler, Measure, Integration & Real Analysis, Graduate Texts in Mathematics (available online).
Elias M. Stein and Rami Shakarchi, Real Analysis: Measure Theory, Integration, and Hilbert Spaces, Princeton Lectures in Analysis.
Association in the course directory
MSTM
Last modified: We 10.04.2024 13:46