MLconf London 2016
Date and time
Location
The British Library
96 Euston Road London NW1 2DB United KingdomRefund Policy
Description
MLconf was created to host the thought leaders in Machine Learning and Data Science to discuss their most recent experience with applying techniques, tools, algorithms and methodologies to the seemingly impossible problems that occur when dealing with massive and noisy data. MLconf is independent of any outside company or university – it’s simply a conference organized to gather the Machine Learning communities in various cities to share knowledge and create an environment for the community to coalesce.
Event Speakers:
Ted Willke, Sr Principal Engineer, Intel
Florian Douetteau, Chief Executive Officer, Dataiku
Abstract: Software Patterns to Scale (Human) Data Science
Flavian Vasile, Senior Data Scientist, Criteo
Sebastian Blohm, Senior Applied Researcher, Microsoft
Abstract: De-Cluttering your Mailbox with Bayesian Inference
Dr. Danny Bickson, Co-Founder, Dato & VP, EMEA
Sunila Gollapudi, Vice President of Technology, Broadridge Financial Solutions
Abstract: Ontology Driven Data Integration Using Machine Learning Techniques
Eyal Kazin, Ph.D, Senior Data Scientist, Cambridge Analytica
Abstract: Modelling Personality Traits on Nationwide Scales
Samantha Kleinberg, Assistant Professor of Computer Science, Stevens Institute of Technology
Abstract: Casual Inference and Explanation to Improve Human Health
We're calling for speakers here! Check out the topics we're interested in. We're particuarly interested in including talks that explain the recent application of Machine Learning models, tools and techniques and their effects of the fintech industry and financial markets. See mlconf.com to see past event speakers and abstracts!
Sponsors:
Gold: Winton Capital, Criteo, MapR
Silver: HiringSolved, Dato
Organized by
MLconf - The Machine Learning Conference gathers communities to discuss the recent research and application of Algorithms, Tools, and Platforms to solve the hard problems that exist within organizing and analyzing massive and noisy data sets.