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| || Date || First meeting: Tuesday 01.06.2022, 14-15, Presentation: TBA || | || Date || First meeting: Thursday 01.06.2023, 14-15, Presentation: TBA || |
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| || Course Page || https://isis.tu-berlin.de/course/view.php?id=34525 || | |
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| || Credits || 3 ECTS, Elective in the modules "Machine Learning I/II", "Deep Learning I/II", and "Cognitive Algorithms" || | |
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| Interested students are required to participate in the kick-off meeting after which they will select, read, understand, and (if possible) programmatically evaluate one of the eligible papers (TBA), before giving a final 10-15 min presentation in the English language at the end of the semester. More details will be discussed during the Kick-off meeting. | Interested students are required to participate in the kick-off meeting after which they will select, read, understand, and (if possible) programmatically evaluate one of the eligible papers (TBA), before giving a final 10-15 min presentation in the English language at the end of the semester. More details will be discussed during the Kick-off meeting. The Zoom-link for the Kick-Off meeting is written on the ISIS course-webpage. |
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| * Federated Learning and Federated Data Preparation. | * Federated/Deep Ensemble Learning and Data Management Systems. |
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| * Compression of ML Systems: From pruning through knowledge distillation to quantization. | * Compression of ML Systems. |
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| * Scalable Bayesian Learning. | |
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''' This seminar cannot be taken as a standalone course. It can only be taken as part of ML-X, DL-X, or CA''' |
Joint Seminar on Machine Learning and Data Management Systems
Date |
First meeting: Thursday 01.06.2023, 14-15, Presentation: TBA |
Room |
First meeting: Via Zoom (TBA), Presentation: MAR4.033 |
Trainers |
Matthias Böhm, Dennis Grinwald |
Course Page |
|
Contact |
This is a joint research-oriented seminar of the Machine Learning Group and the Data Management Group. Throughout the seminar, students will have the opportunity to learn about recent advances in the intersection of Machine Learning and Data Management Systems.
Interested students are required to participate in the kick-off meeting after which they will select, read, understand, and (if possible) programmatically evaluate one of the eligible papers (TBA), before giving a final 10-15 min presentation in the English language at the end of the semester. More details will be discussed during the Kick-off meeting. The Zoom-link for the Kick-Off meeting is written on the ISIS course-webpage.
Example topics include:
- Federated/Deep Ensemble Learning and Data Management Systems.
- Carbon-aware data management systems and Machine Learning.
- Compression of ML Systems.
- Continual, Lifelong, and Online Learning.
- Data management systems for Lifelong Learning.
- Hashing and sketches.
- Building ML pipelines for large large scale data preparation, model training and model debugging, versioning, and monitoring.
- AutoML.