Joint Seminar on Machine Learning and Data Management Systems

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.

Example topics include:

• Federated Learning and Federated Data Preparation.
• Carbon-aware data management systems and Machine Learning.
• Compression of ML Systems: From pruning through knowledge distillation to quantization.
• Continual, Lifelong, and Online Learning.
• Data management systems for Lifelong Learning.
• Hashing and sketches.
• Scalable Bayesian Learning.
• Building ML pipelines for large large scale data preparation, model training and model debugging, versioning, and monitoring.
• AutoML.

Edit conflict - other version:

This seminar cannot be taken as a standalone course. It can only be taken as part of ML-X or DL-X

Edit conflict - your version:

This seminar cannot be taken as a standalone course. It can only be taken as part of ML-X, DL-X, or CA-X

End of edit conflict