We are excited and grateful to be hosted by Accenture at The Dock for Meetup number #12. We're pleased to present another great line up of speakers from Accenture, Barnett Waddingham LLP and ADAPT Centre (Trinity). Discussing the El Dorado competition ran by Dublin R last year. Unlike Kaggle which focuses on predictive accuracy, this competition tried to bring in more real-world aspects of the job. Understanding who we are, and not just what we like, is key to enabling truly personalised services and experiences.

At the last meetup we mentioned that we have created a survey to understand your experience of the meetup, what you want get from it and how we should develop it in the future. We (the meetup organisers) would be very grateful if you could take a few minutes to complete the survey. We will leave it live for a couple more weeks and let you know some of the main findings at the next meetup (which will be in Accenture on 27th March). Just click the bit.ly/mldublin link to get started or paste this link into a browser: bit.ly/mldublin

This month we are very proud to have Zalando as host, at their nice Dublin HQ in Barrow Street. A Graphical Approach to Social Media Analysis" [19:20 - 19:55] Speaker To Be Confirmed. IMPORTANT NOTES: • This event is strictly for Machine Learning professionals and students only • If you finally can't make it, please RSVP to "NO" as soon as possible so that other people can take your place.

This month we are very proud to have Bank of Ireland as host, in their nice event space in Grand Canal Square. IMPORTANT NOTES: • This event is strictly for Machine Learning professionals and students only • If you finally can't make it, please RSVP to "NO" as soon as possible so that other people can take your place.

This month we will talk about Applications of Deep Learning in this event hosted by our amazing sponsor Boxever (Customer Intelligence Cloud for Travel). Online booking sites for example in the travel industry often use personalised recommendations to drive conversions. The ability to link behavioural clickstream data with transactional order data affords us the opportunity to utilise feedback online with respect to the recommendations made. For example, it is common to deploy a recommendation model, test it as part of an A/B testing framework, and evaluate retrospectively for which customers the model worked and for which ones it didn't. This is often a manual process and as a consequence may fail to exploit the richness in the behavioural and transactional features.