Advanced Practical Course - Application Challenges for Machine Learning on IBM Power Architecture (IN2128, IN2106, IN212810)
Master-Praktikum - Application Challenges for Machine Learning am Beispiel von IBM Power Architecture (IN2128, IN2106, IN212810)

Course 0000003522 in WS 2020/1

General Data

Course Type

practical training

Semester Weekly Hours

6 SWS

Organisational Unit

Informatics 17 - Chair of Information Systems and Business Process Management (Prof. Rinderle-Ma)

Lecturers

Simon Fuchs
Holger Wittges

Dates

7 singular or moved dates

iCalendar

	Date and Time	Room and Remark
!	Tue, 2020-11-03, 09:00–16:00	Woche 1 - Block 1 Speaker: Simon Fuchs M.Sc.
!	Wed, 2020-11-04, 09:00–16:00	Woche 1 - Block 2 Speaker: Simon Fuchs M.Sc.
!	Tue, 2020-11-10, 13:00–17:00	Woche 2 - Block 1 Speaker: Simon Fuchs M.Sc.
!	Wed, 2020-11-11, 13:00–17:00	Woche 2 - Block 2 Speaker: Simon Fuchs M.Sc.
!	Wed, 2020-11-25, 14:00–17:00	Project Kick-off Speaker: Simon Fuchs M.Sc.
!	Wed, 2020-12-16, 13:00–18:00	Intermediate Presentations Speaker: Simon Fuchs M.Sc.
!	Wed, 2021-02-03, 10:00–16:00	Final Presentations Speaker: Simon Fuchs M.Sc.

Assignment to Modules

IN2106: Master-Praktikum / Advanced Practical Course
This module is included in the following catalogs:
- Further Modules from Other Disciplines

Further Information

Courses are together with exams the building blocks for modules. Please keep in mind that information on the contents, learning outcomes and, especially examination conditions are given on the module level only – see section "Assignment to Modules" above.

additional remarks	Artificial intelligence, machine learning and artificial neural networks are the growth areas of our time. Self-learning algorithms, trained systems and semi-autonomous data evaluation enable companies to benefit from their company data like never before. The steady increase in data growth combined with a shorter time to market, especially for software products, requires more and more effective and efficient data analysis. In order to make this possible, not only advanced AI frameworks are required, but also modern hardware architectures that perform such analyses. For example, support with high memory bandwidths and GPU accelerators. With its Visual Insights product, IBM offers a comprehensive range of coordinated software and hardware that offer and support solutions for specifically such emerging scenarios. If you are interested in the results of last year's course, you may also have a look at our homepage: https://openpower.ucc.in.tum.de/research/teaching-and-practical-courses/
Links	Course documents E-Learning course (e. g. Moodle) Additional information TUMonline entry

additional remarks

Artificial intelligence, machine learning and artificial neural networks are the growth areas of our time. Self-learning algorithms, trained systems and semi-autonomous data evaluation enable companies to benefit from their company data like never before. The steady increase in data growth combined with a shorter time to market, especially for software products, requires more and more effective and efficient data analysis. In order to make this possible, not only advanced AI frameworks are required, but also modern hardware architectures that perform such analyses. For example, support with high memory bandwidths and GPU accelerators. With its Visual Insights product, IBM offers a comprehensive range of coordinated software and hardware that offer and support solutions for specifically such emerging scenarios. If you are interested in the results of last year's course, you may also have a look at our homepage: https://openpower.ucc.in.tum.de/research/teaching-and-practical-courses/

Links

Course documents
E-Learning course (e. g. Moodle)
Additional information
TUMonline entry

Equivalent Courses (e. g. in other semesters)

Semester	Title	Lecturers	Dates
WS 2021/2	Advanced Practical Course - Application Challenges for Machine Learning on IBM Power Architecture (IN2128, IN2106, IN212810)	Fuchs, S. Wittges, H.	singular or moved dates
WS 2019/20	Advanced Practical Course - Application Challenges for Machine Learning on the Example of IBM Power AI (IN2128, IN2106, IN212810)	Kienegger, H. Levkovskyi, B. Pfaff, M. Rank, J. Responsible/Coordination: Krcmar, H.	Tue, 09:00–16:00, MI 01.13.034 Mon, 09:00–13:00, MI 01.13.034 and singular or moved dates