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:

Irina Rish, Research Staff, IBM T.J. Watson Research Center

Abstract: Learning About Brain: Sparse Modeling and Beyond

Alex Smola, Professor in the Machine Learning Department, Carnegie Mellon University

 Abstract: Fast, Cheap and Deep - Scaling Machine Learning

Xavier Amatriain, VP of Engineering, Quora

 Abstract: 10 More Lessons Learned from Building Real-Life ML Systems

Ben Hamner, Co-founder and CTO, Kaggle

Abstract: Lessons Learned from Running Hundreds of Kaggle Competitions

Allison Gilmore, Data Scientist, Ayasdi

Abstract: A Role for Topology in Data Science

Isabelle Guyon, President at ChaLearn

Abstract: Network Reconstruction: The Contribution of Challenges in Machine Learning

Quoc Le, Software Engineer, Google 

Abstract: Deep Learning: Overview and Latest Results 

Justin Basilico, Research/Engineering Manager, Netflix

Abstract: Recommendations for Building Machine Learning Software

Subutai Ahmad, VP of Research, Numenta

Abstract: Real-time Anomaly Detection for Real-time Data Needs

Brad Klingenberg, Director of Styling Algorithms, Stitch Fix

Abstract: Combining Statistics and Expert Human Judgement for Better Recommendations

Braxton McKee, CEO & Founder, Ufora

Abstract: Is Machine Learning Code for 100 Rows or a Billion the Same?

Narayanan Sundaram, Research Scientist, Intel Labs

 Abstract: GraphMat: Bridging the Productivity-Performance Gap in Graph Analytics

Alessandro Magnani, Research Scientist, Walmart Labs

 Abstract: Classification Labels in a Fast Moving Environment

Animashree Anandkumar, Electrical Engineering and CS Dept, UC Irvine

Tensor Methods: A New Paradigm for Training Probabilistic Models, Neural Networks and Reinforcement Learning

Melanie Warrick, Deep Learning Engineer, Skymind.io

Abstract: Attention Neural Net Model Fundamentals

Event Emcees:

Josh Wills, Director of Data Engineering, Slack

 Event Emcee

Ted Willke, Sr Principal Engineer, Intel

 Event Emcee

 Eric Battenberg, Research Scientist, Baidu Silicon Valley Artificial Intelligence Lab (SVAIL)

 Event Emcee

Event Sponsors:

Platinum: IBM

Gold: MapR, Cloudera ,h2o.ai, Netflix, Ayasdi, Stich Fix, Metis, Urthecast, @WalmartLabs, SAS

Silver: Numenta, Google, Galvanize, Intel

Media: O'Reilly, Basic Books, CRC Press, MIT Press

Video Footage Provided By: Welch Labs