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Deep Learning

Module name (EN):
Name of module in study programme. It should be precise and clear.
Deep Learning
Degree programme:
Study Programme with validity of corresponding study regulations containing this module.
Applied Informatics, Master, ASPO 01.10.2017
Module code: PIM-DL
SAP-Submodule-No.:
The exam administration creates a SAP-Submodule-No for every exam type in every module. The SAP-Submodule-No is equal for the same module in different study programs.
P221-0155
Hours per semester week / Teaching method:
The count of hours per week is a combination of lecture (V for German Vorlesung), exercise (U for Übung), practice (P) oder project (PA). For example a course of the form 2V+2U has 2 hours of lecture and 2 hours of exercise per week.
2V+2P (4 hours per week)
ECTS credits:
European Credit Transfer System. Points for successful completion of a course. Each ECTS point represents a workload of 30 hours.
6
Semester: 3
Mandatory course: no
Language of instruction:
English
Assessment:


[still undocumented]
Applicability / Curricular relevance:
All study programs (with year of the version of study regulations) containing the course.

E2831 (P221-0155) Electrical Engineering and Information Technology, Master, ASPO 01.04.2019 , optional course, technical
KIM-DL (P221-0155) Computer Science and Communication Systems, Master, ASPO 01.10.2017 , optional course, informatics specific
PIM-DL (P221-0155) Applied Informatics, Master, ASPO 01.10.2017 , semester 3, optional course, informatics specific
Workload:
Workload of student for successfully completing the course. Each ECTS credit represents 30 working hours. These are the combined effort of face-to-face time, post-processing the subject of the lecture, exercises and preparation for the exam.

The total workload is distributed on the semester (01.04.-30.09. during the summer term, 01.10.-31.03. during the winter term).
60 class hours (= 45 clock hours) over a 15-week period.
The total student study time is 180 hours (equivalent to 6 ECTS credits).
There are therefore 135 hours available for class preparation and follow-up work and exam preparation.
Recommended prerequisites (modules):
PIM-DS Data Science


[updated 15.11.2021]
Recommended as prerequisite for:
Module coordinator:
Prof. Dr. Klaus Berberich
Lecturer: Prof. Dr. Klaus Berberich

[updated 19.02.2020]
Learning outcomes:


[still undocumented]
Module content:


[still undocumented]
Recommended or required reading:


[still undocumented]
Module offered in:
SS 2024, SS 2023, SS 2022, SS 2021, SS 2020 
[Thu Nov 21 09:46:02 CET 2024, CKEY=kdl, BKEY=pim2, CID=PIM-DL, LANGUAGE=en, DATE=21.11.2024]