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:

Jorge A. Castañón, Data Scientist, IBM

Abstract: What is RedRock? Data + ML + Design

Maja Kabiljo, Software Engineer, Facebook

Abstract: Large-scale item recommendations with Apache Giraph – This is a joint work with Aleksandar Ilic, Facebook Inc.

Pedro Domingos, Professor, University of Washington

Abstract: The Five Tribes of Machine Learning, and What You Can Take From Each

Jennifer Marsman, Principal Developer Evangelist, Microsoft

Abstract: Fun with Mind Reading

Allison Gilmore, Data Scientist, Ayasdi

Abstract: Topological Learning with Ayasdi

Jorge Silva, Sr. Research Statistician Developer, SAS

Abstract: Estimating the Number of Clusters in Big Data with the Aligned Box Criterion

Ted Dunning, Chief Application Architect, MapR

Abstract: Complement Deep Learning with Cheap Learning

Hank Roark, Data Scientist, H2O

Abstract: Generalized Low Rank Models for Unsupervised Learning

David Talby, SVP Engineering, Atigeo

Abstract: Fraud detection is a classic adversarial analytics challenge

Dale Smith, Data Scientist, Nexidia

Abstract: Tensor Decompositon and Machine Learning

Sven Kreiss, Lead Data Scientist, Wildcard

Abstract: Deep ML Architecture at Wildcard

Anusua Trivedi, Data Scientist at Texas Advanced Computing Center (TACC), UT Austin, Atigeo

Abstract: Building a Recommender System for Publications using Vector Space Model and Python

Jason Huang, Solutions Engineer, Qubole

Abstract: Sparking Data in the Cloud

Raji Balasuubramaniyan, Senior Data Scientist, Manheim

Abstract: Leveraging Machine Learning Techniques for Vehicle Auction Industry

Ce Zhang, Postdoctoral Researcher, Stanford University

Abstract: Caffe con Troll: Shallow Ideas to Speed Up Deep Learning

Event Sponsors:

Platinum Sponsor: IBM

Gold Sponsor: MapR, h2o.ai, Qubole, Insightpool, SAS

Silver Sponsor: LogicBlox

Media Sponsor: CRC, Basic Books