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Module Number:
| 14445
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| Module Title: | Seminar Computing at Scale in Machine Learning |
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Seminar Computing at Scale im maschinellen Lernen
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| Department: |
Faculty GW - Faculty of Health Sciences Brandenburg
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| Responsible Staff Member: | -
Prof. Dr. rer. nat. Schliep, Alexander
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| Language of Teaching / Examination: | English |
| Duration: | 1 semester |
| Frequency of Offer: |
Every summer semester
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| Credits: |
4
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| Learning Outcome: | After successfully completing the module, students will be familiar with state-of-the-art problems and methodological approaches in the intersection of large-scale computing and machine learning; that is, computing at scale for machine learning and machine learning for large-scale computation. They will have the ability to familiarize themselves with current research in the area from original research literature, to participate in a technical discussion within the context of international science, and present scientific content in written and oral form. |
| Contents: | Students will learn about specific state-of-the-art problems and methodological approaches for large-scale computation in machine learning. The topics range from parallel computation on individual machines, to implicit parallelism frameworks on compute clusters, and algorithms and data structures supporting efficient exact or approximate computation with massive data sets in and out of core.
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| Recommended Prerequisites: | - Knowledge of the content of the module 14038 Computing at Scale in Machine Learning: Distributed Computing and Algorithmic Approaches
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| Mandatory Prerequisites: | |
| Forms of Teaching and Proportion: | -
Seminar
/ 2 Hours per Week per Semester
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Research paper/essay
/ 30 Hours
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Self organised studies
/ 60 Hours
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| Teaching Materials and Literature: | Depending on the projects, the relevant literature or material will be shared in the first session of the semester. |
| Module Examination: | Continuous Assessment (MCA) |
| Assessment Mode for Module Examination: | - Seminar presentation, 30-45 min depending on the subject (40%)
- Report on the topic of the seminar presentation, 10 pages (40%)
- Active participation (20%)
A student passed the module, if he/she achieves 75% of the total.
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| Evaluation of Module Examination: | Study Performance – ungraded |
| Limited Number of Participants: | 20 |
| Part of the Study Programme: | -
Master (research-oriented) /
Artificial Intelligence /
PO 2022
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Master (research-oriented) /
Informatik /
PO 2008
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| Remarks: | - Study programme Artificial Intelligence M.Sc.: Compulsory elective module in complex „Seminars or Laboratories“
- Study programme Informatik M.Sc.: Compulsory elective module in complex "Seminare oder Praktika" (level 400)
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| Module Components: | - Seminar Computing at Scale in Machine Learning
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| Components to be offered in the Current Semester: | |
