XGBoost (https://xgboost.ai/) (https://github.com/dmlc/xgboost), one of the most popular Gradient Boost Tree Systems, is widely adopted as a solution for data science competitions, like Kaggle, & many production scenarios at companies like Uber, Airbnb, Netflix, etc.

Join us for an evening of tech talks & networking with speakers from Uber, Amazon, & Ant Financial around leveraging this powerful ML tool in production environments.

Topics & Speakers:
Machine Learning with Large-scale Telematics Data: Wayne Zhang & Gorkem Ozkaya [Uber]
The increasing availability of mobile phones with embedded GPS devices & sensors has spurred the use of vehicle telematics in recent years. Telematics provides information on a vehicle, such as the location, speed, & movement. Additional insights can be learned from combining vehicle telematics with other spatial data. In this talk, we will explain how it is possible to transform massive & noisy telematics datasets into a structured form using a combination of manual feature engineering & deep learning approaches & then leveraging XGBoost for a set of classification & regression problems.

Learning Deep Forest Using XGBoost: Yin Lou [Ant Financial]
Deep forest is a recently proposed deep learning framework which uses tree ensembles as its building blocks. In this work, we develop the distributed version of deep forest model with XGBoost as the basic block. We present lessons & learnings of building such machine learning systems & demonstrate its effectiveness on two applications at Ant Financial; fraud detection & customer purchase behavior prediction.

Come learn more about XGBoost & how its utilized today!

Agenda:
6:00PM-6:45 PM: Doors Open
6:45PM-7:00 PM: Introduction | Speakers: Nan Zhu [Uber] & Philip Cho [Amazon]
7:00PM-7:30 PM: Machine Learning with Large-scale Telematics Data: Wayne Zhang & Gorkem Ozkaya [Uber]
7:30PM-8:00PM: Learning deep forest using XGBoost: Yin Lou [Ant Financial]
8:00PM-8:30 PM: Q&A & Networking

This is an event you dont want to miss!