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Module Number:
| 14449
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| Module Title: | Workflows for Machine Learning and Reproducible Science Laboratory |
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Praktikum Workflows für maschinelles Lernen und reproduzierbare Wissenschaft
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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: |
On special announcement
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| Credits: |
6
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| Learning Outcome: | After successfully completing the module, students will have acquired practical skills in developing and implementing machine learning workflows. They will be able to apply state-of-the-art-tools to utilize machine learning for reproducible science through reproducible, replicable, scalable, and portable workflows, related visualization and writing tools, and techniques relevant to implementing GDPR provisions for sensitive data. |
| Contents: | The laboratory is implemented as one or several study projects using workflows for ML and science and related tools. For each project the following work steps have to be accomplished:
- Getting acquainted with the state of the art
- Project planning
- Selecting the models and mechanisms to be used
- Implementation of a prototype
- Testing and evaluating the prototype
- Documentation
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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
- good knowledge of Python and Linux command line
- basic knowledge of machine learning methods
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| Mandatory Prerequisites: | |
| Forms of Teaching and Proportion: | -
Practical training
/ 2 Hours per Week per Semester
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Study project
/ 90 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: | - Executable and tested prototype (50% of total marks)
- Complete documentation, 10-20 pages (20% of total marks)
- Successful intermediate presentation of results, 10 min (10% of total marks)
- Successful final presentation of results, 20 min (20% of total marks)
A student passed the module, if he/she achieves 75% of the total. |
| 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: | - Laboratory Workflows for Machine Learning and Reproducible Science Laboratory
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| Components to be offered in the Current Semester: | |
