ML & Beer: Collecting training data when you have none

Any good ML model is based on the right dataset. But how do you get high-quality labeled data without getting grey hair? Three speakers share real-world experiences of how they navigated figuring out what data were needed and finding strategic ways to get it.

Speakers:
Jonas Moll, PhD in Health Informatics, CEO of Rehfeld Medical
"Combining closed and open datasets."

Eric Navarro, Machine Learning Engineer at Radiobotics
"Fake it till you make it: Machine learning with sparse data."

Maria de Freitas, Growth Lead of Imagine Project, LEO Innovation Lab
"Using growth hacking to accelerate the accuracy of your ML models."

Akshay Pai, Co-founder and CTO, Cerebriu
"Handling radiology data: garbage in, garbage out."

Drinks and snacks will be served. We hope to see you there!