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

Course 0000003522 in WS 2019/20

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

Harald Kienegger
Borys Levkovskyi
Matthias Pfaff
Johannes Rank
Responsible/Coordination: Helmut Krcmar

Dates

Tue, 09:00–16:00, MI 01.13.034
Mon, 09:00–13:00, MI 01.13.034
and 3 singular or moved dates

iCalendar

	Date and Time	Room and Remark
	Tue, 2019-10-15, 09:00–16:00	Boltzmannstr. 3 Introduction
	Wed, 2019-10-16, 09:00–16:00	Boltzmannstr. 3 Introduction
	Mon, 2019-10-28, 09:00–13:00	Boltzmannstr. 3 Introduction Speaker: Borys Levkovskyi M.Sc.
	Tue, 2019-10-29, 09:00–13:00	Boltzmannstr. 3 Introduction Speaker: Borys Levkovskyi M.Sc.
!	Tue, 2019-11-05, 13:00–16:00	Boltzmannstr. 3 Project Kickoff
!	Tue, 2019-12-03, 13:00–17:00	Boltzmannstr. 3 Intermediate Presentation
!	Tue, 2020-01-28, 10:00–16:00	Boltzmannstr. 3 Final Presentation

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	The latest hardware and software technologies and solutions help to reduce time-to-market of new products. The increasing growth of data combined with shorter development cycles – especially for software products – requires more effective and efficient data analyses. To make this possible, not only advanced AI frameworks are required, but also modern hardware architectures that support such analyses, e.g. through high memory bandwidths and GPU accelerators, are necessary. Companies have economic values in their data that remain largely unused at present since the necessary processes are not yet established and corresponding applications for analysis and evaluation with powerful hardware are missing. As a result, the sensible use and economic value of data cannot yet be fully exploited. Therefore, the ability to create targeted data analyses is one of the core tasks of the "Data Scientist". This includes a fundamental understanding of the actual machine learning algorithms, their application to modern hardware architectures with the aid of modern accelerators and the associated processes (DevOps). Only if an optimal coordination between development and operation is established, the best possible analyses can be achieved and the full potential of the hardware can be exploited. With its PowerAI products, IBM offers a comprehensive range of software and hardware that offer and support solutions for precisely such emerging scenarios.
Links	Course documents E-Learning course (e. g. Moodle) TUMonline entry

additional remarks

The latest hardware and software technologies and solutions help to reduce time-to-market of new products. The increasing growth of data combined with shorter development cycles – especially for software products – requires more effective and efficient data analyses. To make this possible, not only advanced AI frameworks are required, but also modern hardware architectures that support such analyses, e.g. through high memory bandwidths and GPU accelerators, are necessary. Companies have economic values in their data that remain largely unused at present since the necessary processes are not yet established and corresponding applications for analysis and evaluation with powerful hardware are missing. As a result, the sensible use and economic value of data cannot yet be fully exploited. Therefore, the ability to create targeted data analyses is one of the core tasks of the "Data Scientist". This includes a fundamental understanding of the actual machine learning algorithms, their application to modern hardware architectures with the aid of modern accelerators and the associated processes (DevOps). Only if an optimal coordination between development and operation is established, the best possible analyses can be achieved and the full potential of the hardware can be exploited. With its PowerAI products, IBM offers a comprehensive range of software and hardware that offer and support solutions for precisely such emerging scenarios.

Links

Course documents
E-Learning course (e. g. Moodle)
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 2020/1	Advanced Practical Course - Application Challenges for Machine Learning on IBM Power Architecture (IN2128, IN2106, IN212810)	Fuchs, S. Wittges, H.	singular or moved dates