Women's day special with Spotify on user personalisation & Deep Learning & Art

Welcome to the first of many women in machine learning meetup! AirBnb has been kind enough to host us in their really cool office space for this event.
We are very pleased to have a talk from air bnb as well as a talk on neural style transfer. An amazing speaker from Spotify will be joining us to talk about their user experience and playlist consumption.
Come celebrate women's day with us and join us for the very first London chapter for women in machine learning!!

The agenda for the meetup with be as follows:

Networking from 6.30 and talks follow from 7 pm.

Speaker : TBC (AirBnb)

Title: TBC

Speaker: Mounia Lalmas-Roelleke (Spotify)

Title: Personalising the user experience and playlist consumption on spotify

Description:

Mounia Lalmas is a Director of Research at Spotify, and the Head of Tech Research in Personalization. Mounia also holds an honorary professorship at University College London. Before that, she was a Director of Research at Yahoo, where she led a team of researchers working on advertising quality. She also worked with various teams at Yahoo on topics related to user engagement in the context of news, search, and user generated content. Prior to this, she held a Microsoft Research/RAEng Research Chair at the School of Computing Science, University of Glasgow. Before that, she was Professor of Information Retrieval at the Department of Computer Science at Queen Mary, University of London. Her work focuses on studying user engagement in areas such as advertising, digital media, social media, search, and now music.

The topic:
The aim of the Personalisation mission at Spotify is “to match fans and artists in a personal and relevant way”. In this talk, Mounia will describe some of the (research) work to achieve this, from using machine learning to metric validation and evaluation methodology. She will describe works done in the context of Home.

Speaker: Roshini Johri

Title: Neural style transfer

Description:
Learn how to create art with deep networks. We will use this session to understand how to build a network that can train on artwork and use the painters style to create something new. Care to Van Gogh? Related paper to the project: https://arxiv.org/abs/1508.06576
We will be exploring tensor flow's eager exploration method, understand how to evaluate intermediate layers and if time permits explore some fun architectures! Code will be shared and questions well be welcomed!