Actions Panel
MLconf SF 2015
When and where
Date and time
Location
Julia Morgan Ballroom 465 California Street San Francisco, CA 94104
Map and directions
How to get there
Refund 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:
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