NOTE: A valid photo ID is required by building security. You MUST use your full real names on your meetup profile, otherwise, you will NOT make it on the guest list!
Tickets are assigned through a lottery draw initially. Waitlist places are assigned manually. We can only admit you if you use your full real names on your meetup profile.
If your RSVP status says "You're going" you will be able to get in. No further confirmation required. You will NOT need to show your RSVP confirmation when signing in.
If you can no longer make it, please unRSVP as soon as you know so we can assign your place to someone on the waiting list.
As always, there'll be free food & drinks, generously provided by our host, AHL.
We are issuing tickets via a lottery - if you want to be in with a chance of a place - sign up for the waitlist! The lottery will be run approx 1 week before the meetup, and we will re-run the lottery to fill any spaces that free up or use the waitlist towards the time of the event.
Mark Pinkerton -- Oasis: an open source framework for the modelling of natural disaster risk
Open catastrophe modelling using Oasis LMF
- history and use of catastrophe models in (re)insurance, and the Oasis mission to provide choice and insight through open software and standards
- anatomy of a cat model - simple example using Jupyter, Pandas, Bokeh + the oasislmf package
- the future - understanding model uncertainty, application of ML to model building, new data sources
Kevin Machado -- AI over auscultation: improving the early diagnostic
This project is about how we are contributing to the early diagnostic of Cardiovascular disease improving a basic medical tool through the use of signal processing techniques, user interface design and machine learning.
Eyal Kazin -- Protein Design by Multi-Objective Optimisation
In this talk I will discuss Pareto Optimisation, a method for finding optimal solutions of multiple objective functions, and demonstrate an application in protein design.
Rafah El-Khatib -- Leave-One-Feature-Out Importance
The LOFO (leave one feature out) importance calculates the importances of a set of features based on a metric of choice, for a model of choice, by iteratively removing each feature from the set, and evaluating the performance of the model, cross-validated, based on the chosen metric.
Doors open at 6.30pm (get there early as you have to sign-in via AHL's security), talks start at 7 pm, drinks from 9 pm in the bar. We normally have >200 folks in the room so there's plenty of people to discuss data science questions with!
Please unRSVP in good time if you realise you can't make it. We're limited by building security on the number of attendees, so please free up your place for your fellow community members!
Follow @pydatalondon (https://twitter.com/pydatalondon) for updates and early announcements.