<aside> 🔻 Slides and interactive notebook available

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Format

• What? An in-person, hands-on, free workshop introducing machine learning
• For whom? PhD students, postdocs, and PIs affiliated with the University of Tübingen
• When? July 6th, 2022, from 9:00 to 17:00 (with pauses)
• Where? Max Planck Institute for Intelligent Systems, Lecture hall (ground floor), Max-Planck-Ring 4, 72076 Tübingen
• Instructors? Alexandra Gessner, Seth Axen, ‣, @ML ⇌ Science Colaboratory, Cluster of Excellence *ML for Science.*

Content

In the tutorial part, we will discuss with the aid of interactive demonstrations topics such as

• Foundational ML notions: data features, model complexity, generalization, evaluation of performance, ...
• Neural networks: gradient-based training, feature extraction, architectures, transfer learning, …
• Data types: text, time series, relational data, images, ...
• Programming ML models vs. traditional software.
• The ML workflow in practice, from data collection to performance evaluation.
• Limitations of ML for science.

In the workshop part, we will jointly look at specific use-cases brought by the attendees and discuss how to design ML projects to address them.

Prerequisites

• Scholars from all disciplines are warmly invited to join; no math or programming skills will be assumed.
• Bring your own laptop, and please check your eduroam config before coming!
• In connection to one of your research problems, think about the characteristics of relevant datasets that you have access to (how many records, what fields? ...) and what you’d like ML to help with. Be ready to discuss your use-case with the group in one single slide; we will provide suggestions to help you make a practical machine learning project out of it.