Ali Jalali presents how to develop a machine learning predictive analytics engine for big data analytics.
Simon Chan shares the on-going challenges, the design dilemma and the steps to be taken when building customized large-scale predictive ML applications on a ML SaaS platform.
Suman Deb Roy talks about some of Betaworks’ internal data tools and platform, product-specific solutions and best practices they learned when machine learning has to drive the startup road.
John Langford discusses how to use Vowpal Wabbit in and as a machine learning system including architecture, unique capabilities, and applications, applied to personalized news recommendation.
Edo Liberty presents some basic concepts and an introduction to the subfields of machine learning and data mining.
David Talby demos using Python libraries to build a ML model for fraud detection, scaling it up to billions of events using Spark, and what it took to make the system perform and ready for production.
Jan Neumann presents how Comcast uses machine learning and big data processing to facilitate search for users, for capacity planning, and predictive caching.
Michael Manapat talks about how to choose, train, and evaluate models, how to bridge the gap between training and production systems, and avoiding pitfalls.
Kristjan Korjus discusses deep learning, reinforcement learning and their combination called deep Q-Network.
Ali Kheyrollahi uses clustering and network analysis algorithms to analyze the publicly available Wiki data on rock music to find mathematical relationship between artists, trends and subgenres.
Alena Hall presents various machine learning algorithms available in Accord.NET - a framework for machine learning and scientific computing in .NET.
Brian Wilt discusses how applied machine learning techniques and data science helped Jawbone build a successful fitness tracking app.