TU BRAUNSCHWEIG
| Carl-Friedrich-Gauß-Fakultät | Informatik
Informatikzentrum

Seminar Verteilte Systeme: System Support for Machine Learning

SemesterWintersemester 2018/2019 [ Andere Semester: ]
Modulnr.INF-VS-041
Studieng.Master Informatik
IBR Gruppe(n)DS (Prof. Kapitza)
ArtSeminar
Dozent
PhotoProf. Dr. Rüdiger Kapitza
Abteilungsleiter
kapitza[[at]]ibr.cs.tu-bs.de
+49 531 3913294
Raum 135
Assistenten
PhotoVasily Sartakov
Wissenschaftlicher Mitarbeiter
sartakov[[at]]ibr.cs.tu-bs.de
+49 531 3913155
Raum 169
PhotoManuel Nieke
Wissenschaftlicher Mitarbeiter
nieke[[at]]ibr.cs.tu-bs.de
+49 531 3913155
Raum 169
LP5
SWS0+2
Ort & Zeit

Kick-Off Meeting: 16.10.2018 16:00, SN22.2

Weekly Meeting: TBD,

Presentation days: 24.01.2019, 25.01.2019, BRICS 107/108

SpracheEnglish
Inhalt

Introduction

Artificial intelligence (AI) in general and machine learning (ML) in particular are hot topics nowadays. They can be applied in many areas of human being and work as disruptive forces that change economies and industries. While AI has been the 'Holy Grail' for computer scientists and an old dream of mankind since the 1950th, it did not experience significant development until modern hardware platforms became available. Heterogeneous systems based on specialised Digital Signal Processors (DSP), various Single Instruction Multiple Data (SIMD) accelerators, and commonly used general-purpose computing for GPU (GPGPU) enabled a significant performance increase of ML algorithms. Without doubts, the further developments of ML/AI systems relies not only on new algorithms but also on new hardware and software platforms. The establishment of new conferences such as SysML, which covers research topics of system research and machine learning, confirms this hypothesis.

In this seminar, we consider machine learning from system research point of view. We will discuss how system layer supports new hardware and used by ML applications, how cloud and distributed platforms are built to provide services for ML/AI applications and more.

Organization

This seminar is organized together with two other seminars that also deal with machine learning:

By this collaboration, you will not only learn more about machine learning attacks and defenses, but you also get an impression of the wide range of machine learning applications, problems and techniques.

Requirements

The seminar is organized like a real academic conference. You need to prepare a written paper (English) about the selected topic.

After submitting your paper at our conference system, you will write two short reviews about two of the papers submitted by your fellow students. In this way, you can give them feedback about how to improve their paper. Then, you will have time to improve your own final paper with reviews from the others.

Last but not least, you will give a 20-25 minutes talk about your paper and we will provide drinks and pizza to enjoy the talks at our small conference.

Papers

Tier 1

Tier 2

Tier 3

Literatur/Links

Usefull links

On World-Wide-Web, there are different ways to enhance your seminar presentations:

The following databases can help you with the literature review:

LaTeX is a document preparation system commonly used for scientific writing. These tutorials can help you:


aktualisiert am 17.10.2018, 09:31 von Vasily Sartakov
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