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Schedule: Hardcore Data Science sessions

n this track, we push the envelope of data science, exploring emerging topics and new areas of study made possible by vast troves of raw data and cutting-edge architectures for analyzing and exploring information. We’ll cover topics such as machine learning, natural language parsing, crowdsourcing and algorithm design.
Who should attend: Data scientists, statisticians, data modellers, and analysts with a strong understanding of data science fundamentals; CTOs, Chief Scientists, and academic researchers.

Ballroom AB
Ben Lorica (O'Reilly Media)
Average rating: ***..
(3.67, 3 ratings)
Opening Remarks Read more.
Ballroom AB
Tutorial Please note: to attend, your registration must include Tutorials on Tuesday.
Average rating: ***..
(3.64, 14 ratings)
All-Day: Strata's regular data science track has great talks with real world experience from leading edge speakers. But we didn't just stop there—we added the Hardcore Data Science day to give you a chance to go even deeper. The Hardcore day will add new techniques and technologies to your data science toolbox, shared by leading data science practitioners from startups, industry, consulting... Read more.
Ballroom AB
Alexander Gray (Skytree, Inc.)
Average rating: ***..
(3.08, 12 ratings)
How do you get to high-performance machine learning, achieving truly best-in-class results vs. the competitors in your application? For example, how do the most advanced organizations achieve ultra-high detection rates at scale in critical applications? We'll outline the elements needed, including choices in cluster designs, data layout and prep, ML methods, and computation, with real examples. Read more.
Ballroom AB
Alice Zheng (1977)
Average rating: ****.
(4.29, 14 ratings)
This talk provides an in-depth understanding of characteristics of Big Data along with algorithmic pain points of Big Learning, from which we may draw insights about requirements for the next generation of Big Learning tools. Read more.
Ballroom AB
Henrik Brink (wise.io), Joshua Bloom (GE Digital)
Average rating: ***..
(3.42, 12 ratings)
Going from raw data to reproducible and production-ready machine-learning in data pipelines and applications is an unsolved problem, leaving businesses with their valuable data unused. New algorithms and frameworks aim to improve the situation and this talk will introduce some of these using examples of real-world machine learning projects. Read more.
Ballroom AB
Ted Dunning (MapR)
Average rating: ****.
(4.80, 15 ratings)
There are many practical details involved in building an anomaly detection system. In this presentation, I will describe the major classes of these systems, and show you how to build anomaly detection systems for: * Determining when an event rate shifts * Determining when new topics appear in content streams * Determining when systems with defined inputs and outputs act strangely Read more.
Ballroom AB
Ilya Sutskever (OpenAI)
Average rating: ****.
(4.25, 16 ratings)
Neural Networks (also known as Deep Learning) are biologically inspired machine learning models. In this talk, I will explain what neural networks are, how they work, and how they were used to achieve the recent record-breaking performance on speech recognition and visual object recognition. Read more.
Ballroom AB
Kira Radinsky (eBay | Technion)
Average rating: ****.
(4.08, 12 ratings)
In our highly data-driven environment, businesses are essentially becoming semi-autonomous agents, constantly competing for resources, customers and talent. Read more.
Ballroom AB
Magda Balazinska (University of Washington)
Average rating: ***..
(3.30, 10 ratings)
Today's Big Data management systems and services are increasingly fast but they are not always easy to use. In this talk, we present recent research results around the theme of facilitating the management and processing of Big Data. Read more.
Ballroom AB
Max Gasner (Salesforce.com)
Average rating: ****.
(4.29, 7 ratings)
Machine learning is still mired in a dark age of one-off solutions and costly expertise. What will it take to build a true predictive platform that is as easy to use -- and, ultimately, as ubiquitous -- as a relational database? We'll touch on the basic design criteria for any candidate predictive platform, the challenges posed by real data, interface and user issues, and promising methods. Read more.
Ballroom AB
Oscar Boykin (Twitter)
Average rating: ****.
(4.36, 11 ratings)
Abstractions are what enable us to think clearly about complex systems. In this talk, we will see how some simple abstractions, such as Monoids, can be used to pattern analytics platforms. Read more.