MACHINELEARNING #DATA #DATACLEANSING #BESTPRACTICE #AI

This event will be in English. In person event. Live stream: https://youtube.com/live/I4Sksd93bZ4

Agenda 18:00 Doors open 18:30 Welcome 18:45 Skrub: Prepping Tables for Machine Learning Gets Easier - Gaël Varoquaux Research Director, Inria, France 19:30 Networking with snacks and beverages 20:15 Using Embeddings and Deep Neural Networks as a technique for AutoML Demand Forecasting - Daniel Stemmer 20:45 Lightning Talks 21:00 Networking with snacks and beverages 21:30 End

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Talk #1 Skrub: Prepping Tables for Machine Learning Gets Easier Gaël Varoquaux, Research Director, Inria, France

In standard data-science practice, a significant effort is spent on preparing the data before statistical learning. One reason is that the data come from various tables, each with its own subject matter, its specificities. These must be transformed to a format that can be injested by machine-learning modeled: assembled, aggregated, encoded. I will present some results from our research in developing machine-learning models that can more easily injest raw, messy data. I will also discuss how we are using this understanding to make a new software package that facilitate preparing tables for machine learning. It's called skrub, it's in progress, not released, but I'm excited!

Gaël Varoquaux is a research director working on data science at Inria (French Computer Science National research) where he leads the Soda teamon computational and statistical methods to understand health and society with data. Varoquaux is an expert in machine learning, with an eye on applications in health and social science. He develops tools to make machine learning easier, suited for real-life, messy data. He co-funded scikit-learn, one of the reference machine-learning toolboxes, and helped build various central tools for data analysis in Python. He currently develops data-intensive approaches for epidemiology and public health, and worked for 10 years on machine learning for brain function and mental health. Varoquaux has a PhD in quantum physics supervised by Alain Aspect and is a graduate from Ecole Normale Superieure, Paris. https://gael-varoquaux.info/about.html

Talk #2 Using Embeddings and Deep Neural Networks as a technique for AutoML Demand Forecasting Daniel Stemmer

Daniel Stemmer studied physics at the KIT Karlsruhe, Diploma 2011, since 2011 data scientist @ BlueYonder, Currently Product Owner of LDE - the Demand Forecasting engine @ BlueYonder

---- Lightning Talks: 1. EU AI ACT\, XAI\, fair ML and banks - Dr. Christoph Anders 2. This is your slot! 3. TBA

Acknowledgements Also a big thank you to our sponsors:

• NUMFOCS, for promoting open source software.
• KÖNIGSWEG, for supporting the organization.
• hei_INNOVATION, for hosting the meetup.

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