Q-Group Sydney Monthly Lunch Series - Application of automated machine learning to financial market problems

Date: 
18 Sep 2019
Speaker: 

Marcus Burley and John Hawkins

Location: 

RedOak Beer Cafe
201 Clarence St, Sydney

Short bio: John Hawkins is a lead data scientist at DataRobot, with over 14 years’ experience in applying machine learning, statistics and other data mining techniques to a diverse range of data sets. Marcus Burley is an Analytics & Quant Sales Specialist with FactSet. Marcus has a Masters of Economics from the University of Sydney with a specialty in Financial Econometrics.

Additional notes: John will give an overview of Automated Machine Learning as a concept and how it has been applied across many industries, particularly banking, insurance and commodities. He’ll identify some of the most resource-intensive aspects of deploying a machine learning solution and explain how these can be overcome with a mix of technology and astute human intervention. Marcus will discuss the unique challenges faced when applying machine learning tools to financial markets. He will present two applied examples, both geared towards enhancing human investment decisions. These examples will show that the most obvious approach can have severe limitations, but that re-framing the objective to a more targeted study can yield better results.

As per usual, we will aim for a 20-minute presentation, followed by lunch and a (lightly) moderated discussion.

If you would like to attend, please rsvp using the following link:

mailto:qrsvp@qgroup.org.au?subject=[SYDNEY]_I_would_like_to_attend_lunch_series_September_2019

We will respond to all rsvps so that you can be certain whether a spot has been allocated.

If you are interested in being the discussion leader for a particular topic, then please contact us at qcommittee@qgroup.org.au. In the long-term, our aim is for most (all?) members to lead a discussion at a lunch series event, so please do not be shy in contacting the committee.

Kind regards,

The Q-Group Committee
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