**Welcome to The AI Meetup, the premier gathering for AI enthusiasts and beyond!**

Whether you're a seasoned professional or just starting out in the field of artificial intelligence, The AI Meetup offers a unique opportunity to connect with like-minded individuals, network with peers, and gain valuable insights into the world of AI.

**Timetable**
18.00 - Kick-off
19.00 - [Onno Zoeter](https://www.linkedin.com/in/onno-zoeter-75330a2a2/), Head of Booking AI Research at Booking.com
19.45 - TBA
20.30 - Pizza and Drinks (Borrel)
21.30 - Let's wrap It up

The AI Meetup covers a wide range of topics and disciplines, from machine learning and natural language processing to computer vision and robotics. So whether you're a data scientist, AI researcher, developer or just someone with a passion for all things AI, you won't want to miss this exciting event.

p.s. There will be food and drinks!

Robert Keus

The AI Meetup

The Designer Meetup

Career & Business

AI and Society

New Technology

Big Data

Data Analytics

Machine Learning

Artificial Intelligence

Data Science

AI Algorithms

Elwin de Witte

Nadja

Jarno

Krishna Iyer

Giovanni Capra

Paolo B.

Gabriela

**When the problem becomes richer than supervised learning. A real-world use of causality in machine learning.**

The classic supervised learning problem that is taught in machine learning courses and is the subject of many machine learning competitions, is often too narrow to reflect the problems that we face in practice.

Historical datasets typically reflect a combination of a source of randomness (for example customers making browsing and buying decisions) and a controlling mechanism such as a ranker or highlighting heuristics (badges, promotions, etc.).

Or there might be a selection mechanism (such as the decision to not accept transactions with high fraud risk) that influences the training data.

A straightforward regression approach would not be able to disentangle the influence of the controller and the phenomenon under study. As a result it risks making incorrect predictions as the controller is changed.
In practice however, such problems are typically treated as a classic regression problem in a first iteration and attempts to identify and correct for these complications come as afterthoughts or are not undertaken at all.

Ideally there is a rigorous and flexible formalism that captures the correct framing of the problem from the very start, accompanied by a set of practical algorithms that work well in practice for each of the identified cases

This research objective is the main goal of the Mercury Machine Learning Lab, one of the labs at Booking AI Research. The Mercury lab is a collaboration between the University of Amsterdam, the Technical University of Delft and Booking.com. It brings together the fields of information retrieval, causality and reinforcement learning where the topic is studied under the names of off-line evaluation, transferability and s-recoverability and off-policy learning respectively.

**This presentation will sketch the problem, highlights some of the theoretical results so far and describes a successful large real-world application.**

Onno Zoeter

The AI Meetup #2: Onno Zoeter (Head of AI Booking.com)

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The AI Meetup

The AI Meetup #2: Onno Zoeter (Head of AI Booking.com)

The AI Meetup

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