Machine Learning: the Extreme & the Explainable

Join us on April 9th at the Belsimpel headquarters in the center of town, Waagstraat 1, for the first PyData Groningen Meetup!

The theme of this first edition will be Machine Learning.

We found two excellent speakers willing to update us on their expertise in this field. Jonathan Alexander will talk about Extreme Data Science. Zeynep Akata will tell us about Explainable Artificial Intelligence. See below for a teaser of these topics and more on the speakers.

Also, we will have a lightning talk session, featuring yourselves!

For this, we invite anyone to prepare a very short talk (ca. 2 minutes) to show some of the cool Python / Data Science work you have done recently. You can use one slide if you want; in that case be early so we can put it on our laptop. First come, first serve!

After the talks our host Belsimpel will generously provide food and drinks so we can continue discussing about our shared interests and meeting new people. See below for the detailed schedule.

Seats are limited, so please RSVP on this event page.

Hope to see you there!

• Patrick Bos & Maarten Breddels

Jonathan Alexander:

Extreme data science is all about creating value in lightning speed. We will demonstrate how to fuse machine learning solutions for unlabelled image data, using the combinations of deep learning, nearest neighbours, graph algorithms, clustering, and page rank to end up with a fast, cheap solution in a breeze.

Jonathan Alexander is the head of data science at KVASS, and founder of the XDSS data-science academy. As a specialist in innovative and deployment of machine learning solutions in the cloud, he has implemented solutions for all machine learning types, like counting cars with images, optimised product pricing, assessing diseases from symptoms, building recommendation engines and more.

Zeynep Akata:

Clearly explaining a rationale for a classification decision to an end-user can be as important as the decision itself. Existing approaches for deep visual recognition are generally opaque and do not output any justification text; contemporary vision-language models can describe image content but fail to take into account class-discriminative image aspects which justify visual predictions. In this talk, I will present my past and current work on Explainable Artificial Intelligence.

Dr. Zeynep Akata is an Assistant Professor with the University of Amsterdam, Scientific Manager of the UvA-Bosch Delta Lab and a Senior Researcher at the Max Planck Institute for Informatics in Germany. Her research interests include machine learning that combine vision and language for the task of explainable artificial intelligence (XAI).

Schedule:

• 17:30 doors open
• 18:00 opening + 2 lightning talks
• 18:15 1st talk
• 18:45 short break, drinks
• 18:55 2nd talk
• 19:25 your lightning talks
• 19:40 have some food and drinks and meet people!
• 21:00 end