Neill will look back at a project from a decade ago: building and deploying a recommender system for a car rental website.
He’ll cover the business problem, the approach he took, the challenges
he encountered, and how the system ultimately made it into production.
Along the way, **mixture models, stochastic gradient descent, discrete
choice modelling, significance testing,** and **purchasing power
parity** will all make an appearance.
Drawing on his experience of taking the project from theory through to
implementation, Neill will share some of the things that worked, some
that didn’t, and lessons that may help you on your next data science
project.
---
Neill Sweeney has a B.Sc. in Applied Mathematics, an M.Sc. in
Mathematics and Physics, and a Ph.D. in Artificial Intelligence, where
he specialised in poker AI.
He was interested in AI before it was cool. Since then, he has worked
on everything from calibrating milk meters to building recommender
systems for car rental websites. He has always been particularly
interested in connecting theory with real-world implementation.
