Meetup #5

Meetup #5

PLEASE NOTE: Limit of 100 attendees (see below)

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 main presentations and their presenters.

Should you wish to contact me, email me at mark.whalley@microfocus.com.

Kindest regards
Mark

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Agenda

18:30 Doors open and networking

18:55 Welcome
Mark Whalley

19:00 Preparing data for machine learning and predictive analytics using Vertica SQL
Mark Whalley

19:30 Applications of Machine Learning Techniques to Algo/Stock Market Trading
Chandini Jain

20:00 Introducing the Seahorse project: quantifying and maximising data value
Michael McGrath

20:30 Beer, Pizza and Networking

21:30 Close

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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.

Mark Whalley
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 Jain
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.

Michael McGrath
Michael is the Chief Strategist for Information Archiving and eDiscovery at Micro Focus. He is a long standing practitioner and award winning researcher with over 20 years experience of machine learning and analytics. He believes that society and organisations benefit when data is properly managed and is initiating a project to promote better data management through data lifecycle management, valuation, interoperability and provenance & robustness.