*Additional details can be found here.
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.
Ted Willke, Sr Principal Engineer, Intel
Abstract: Can Cognitive Neuroscience Provide a Theory of Deep Learning Capacity?
Ewa Dominowska, Engineering Manager, Facebook
Abstract: Generating a Billion Personal News Feeds
Igor Markov, Software Engineer, Google
Abstract: Can AI Become a Dystopian Threat to Humanity? - A Hardware Perspective
Carlos Guestrin, CEO of Dato Inc., Amazon Professor of Machine Learning, University of Washington
Abstract: How Can We Trust Machine Learning? Exploration, Evaluation and Explanation for ML Models
Jake Mannix, Lead Data Engineer, Lucidworks
Abstract: Smarter Search With Spark-Solr
Franziska Bell, Data Science Manager, Uber Technologies
Abstract: Towards 99.99% Availability via Intelligent Real-Time Monitoring
Evan Estola, Lead Machine Learning Engineer, Meetup
Abstract: When Recommendations Systems Go Bad
Amanda Casari, Senior Data Scientist, Concur Technologies
Abstract: Scaling Global Data Science Products, Not Teams
Sam Steingold, Lead Data Scientist, Magnetic Media Online
Abstract: An Information Theoretic Metric for Multi-Class Categorization
Dr. Erin LeDell, Machine Learning Scientist, H2O.ai
Abstract: Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches
Avi Pfeffer, Principal Scientist, Charles River Analytics
Abstract: Practical Probabilistic Programming with Figaro
Kristian Kersting, Associate Professor for Computer Science, TU Dortmund University, Germany
Abstract: Declarative Programming for Statistical ML
Jason Baldridge, Associate Professor of Computational Linguistics, University of Texas at Austin
Abstract: Disambiguating Explicit and Implicit Geographic References in Natural Language
Florian Tramèr, Researcher, EPFL
Abstract: Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit
Suxin Guo, Researcher, eBay
Abstract: Iron: Keyword Grouping Model for Text Ads Bidding in Paid Search
MLTrain: Full-Day Training Event on Saturday 05/21/2016
MLconf 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.
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