Big Data and Machine Learning – London
PLEASE NOTE: Limited to 130 attendees!
Welcome to the next in the BD&ML Meetup series, and what we hope will be another interesting evening of presentations and networking
The agenda is listed below, followed by further details about the presentations and their presenters.
In an attempt to address the high number of no-shows we typically see at Meetups, we kindly request that you register your intent to attend this event via CVENT (http://www.cvent.com/… ).
RSVPing via this Meetup portal is NOT sufficient – only registrations made via CVENT will be admitted!
18:30Doors open and networking
19:00Preparing data for machine learning and predictive analytics using Vertica SQL
19:30Applications of Machine Learning Techniques to Algo/Stock Market Trading
20:00Machine Learning GAN wild: Practical applications of Generative Adversarial Networks
20:30Beer, Pizza and Networking
Preparing data for machine learning and predictive analytics using Vertica SQL
If your data is already in an Enterprise RDBMS, why would you want to extract subsets of that data to perform data exploration and preparation prior to training and testing predictive models if you could do all of this within the database using SQL?
In this presentation, we will look at how Vertica’s in-database data preparation capabilities complement its extensive SQL dialect to simplify data preparation.
We will look at balancing data, detecting outliers, one hot encoding and others.
From the early 1980s, Mark worked with Michael Stonebraker’s Ingres RDBMS and then a number of column-store big data analytic technologies. In 2016, he joined HPE Big Data Platform as a Vertica Systems Engineer, and from September 2017 followed Vertica as it moved over to Micro Focus.
Mark frequently delivers talks at the London, Cambridge and Munich Big Data & Machine Learning Meetups, Vertica Forums and elsewhere, and is a regular blogger on my.Vertica.com.
Applications of Machine Learning Techniques to Algo/Stock Market Trading
ML techniques have found a variety of applications in Trading, this session will attempt to explore some of the ways in which trading problems can be solved using ML techniques. This will be a Python based session and will explore setup of a trading problem, collecting and cleaning data, featuring engineering, model building and validation, and backtesting of results. We will also discuss do’s and don’t and nuances of using ML methods in Algo-Trading.
Chandini is the CEO/ founder of Auquan. She has 6+ years of global experience in finance. She started her career with Deutsche Bank Mumbai/New York and worked as a derivatives trader with Optiver, world’s largest market-maker, in Chicago and Amsterdam from 2013-2016. Since 2017, she has been working on Auquan, an early stage fintech startup bridging the gap between data science and finance. At Auquan, she is employing new and cutting edge ML and Deep Learning techniques to solve financial prediction problems.
Machine Learning GAN wild: Practical applications of Generative Adversarial Networks
Generative Adversarial Networks (GANs) have recently reached few tremendous milestones: generating full-HD synthetic faces, to image compression better than the state of the art to cryptography. I want to talk about some of those most amazing advancements in this field and generally showcase work of other academics.
Jakub graduated from Oxford University and has worked in data science since 2013, most recently as a Data Science Tech Lead at Filtered.com and as a Data Science consultant at Mudano. In addition to his full-time work at small startups and large corporations both as a consultant and an employee, he has finished 20 online courses on data science and related topics. He was previously part of Entrepreneur First.
London – United Kingdom
Tuesday, September 11 at 6:30 PM