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Session Information

Large Scale Machine Learning for Online Services

Speaker(s):

Dr. Ralf Herbrich (Amazon Development Center Germany GmbH )
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Over the past few years, we have entered the world of big and structured data – a trend largely driven by the exponential growth of Internet-based online services such as Search, eCommerce and Social Networking as well as the ubiquity of smart devices with sensors in everyday life. This poses new challenges for statistical inference and decision-making as some of the basic assumptions are shifting including: (1) The ability to store the parameters and data in the cloud, and (2) the level of granularity and ‘building blocks’ in the data modeling phase. In this talk, I will discuss the implications of big and structured data for Statistics and the convergence of statistical model and distributed systems. I will present one of the most versatile modeling techniques that combines systems and statistical properties – factor graphs – and review a series of approximate inference techniques such as distributed message passing. I will also talk about the connection to layered function models known as neural networks. The talk will be interspersed with real-world applications of these techniques in systems such as gamer ranking and matchmaking on Xbox Live (TrueSkill), recommendations and cloud machine learning services (Amazon Machine Learning)./ “TrueSkill is the competitive ranking and matchmaking system used in Halo 3 - player’s skills are the unknown variables learned in real-time from the pairwise or multi-game match outcomes.
Audience adressed: 
Data Scientist, Game Developers
Takeaway: 

(1) What is Machine Learning
(2) What are Graphical Models and Factor Graphs
(3) How does Online Ranking and Matchmaking work
(4) How to convert a statistical data model into an efficient algorithm

Session Type: 
Talk
Track: 
Code / Platforms / Tools
Experience level adressed: 
Intermediate
Time slot: 
2016-04-19 11:00
Room: 
Channel 5

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