Science Park 904, Amsterdam, Netherlands

ADS Deep Dive into Machine Learning

In this Amsterdam Data Science Deep Dive session we will highlight cutting-edge research with a focus on Machine Learning with speakers from academia and industry. There will be an opportunity for audience participation and interaction to discuss the key challenges within this domain.

Date: Friday 13 January 2017

Location: Room C0.05, Science Park 904

To attend please RSVP here


09:15: Coffee

09:30: Welcome and Introduction

Max Welling, Research Chair in Machine Learning, UvA

Speaker 1: Rianne van den Berg, Postdoc in Machine Learning, UvA on Deep Graph Convolutional Recommenders


Speaker 2: Maarten de Rijke, Professor of Information Processing and Internet, UvA on Deep Entity Search 


Speaker 3: Ralf Herbrich, Director of Machine Learning Science Amazon on Machine Learning @ Amazon

10:30: Discussion & Wrap-up

Coffee & Snacks



Amsterdam Data Science accelerates data science research by connecting, sharing and showcasing world-class technology, expertise and talent from Amsterdam on a regional, national and international level. Our research enables business and society to better gather, store, analyse and present data in order to gain valuable insights and make informed decisions.

ADS is an initiative of:

• Informatics Institute (IvI), Universiteit van Amsterdam (UvA)

• Amsterdam Business School and Amsterdam School of Economics, Faculty of Economics and Business, UvA

• Computer Science, Vrije Universiteit (VU) Amsterdam

• Amsterdam University of Applied Sciences (AUAS/HvA)

• Centrum Wiskunde en Informatica (CWI)


We organise meetings and seminars on cutting-edge research, technologies and best practices. These can be our:

– Coffee and Data events which have a broader scope with industry involvement, or

– Deep Dive sessions, which are more research focussed.

We invite you to:

• share the data challenges in your organisation with the ADS community

• discover innovative ways to apply data science across fields

• network and discuss leading issues with students and top data scientists.