Presentation: My Three Ex’s: A Data Science Approach for Applied Machine Learning
This talk is about applying machine learning to solve problems.
It’s not a talk about machine learning — or at least not about the theory of machine learning. Theoretical machine learning requires a deep understanding of computer science and statistics. It’s one of the most studied areas of computer science, and advances in theoretical machine learning give us hope of solving the world’s “AI-hard” problems.
Applied machine learning is more grounded but no less important. We are surrounded by opportunities to apply classifiers, learn rules, compute similarity, and assemble clusters. We don’t need to develop new algorithms for any of these problems — our textbooks and open-source libraries have done that hard work for us.
But algorithms are not enough. Applying machine learning to solve problems requires a data science mindset that transcends the algorithmic details.
In this talk, I’ll communicate the data science mindset by describing my three ex’s: express, explain, and experiment. These three activities are the pillars of a successful strategy for applying machine learning to solve problems. Whether you’re a machine learning novice or expert, I hope you’ll leave this talk with some practical wisdom you can apply to your next project.
Tracks
Covering innovative topics
Monday, 3 November
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Architectures You've Always Wondered about
The newest and biggest Internet architectures
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Real World Functional
Putting functional programming concepts to work in the real world.
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The Future of Mobile
The future of mobile and performance improvements
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Continuous Delivery: From Heroics to Becoming Invisible
Continuous Delivery philosophies, cultures, hiccups, and best practices.
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Unleashing the Power of Streaming Data
This track explores a variety of use-cases, platforms, and techniques for processing and analyzing stream data from the companies deploying them at scale!
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Sponsored Solutions Track I
Tuesday, 4 November
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Engineering for Product Success
Architectures that make products more successful
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Reactive Service Architecture
Reactive, Responsive, Fault Tolerant and More.
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Modern CS In the Real World
How modern CS tackles problems in the real world.
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Applied Machine Learning and Data Science
Understand your big big data!
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Deploying at Scale
Containerizing Applications, Discovering Services, and Deploying to the Grid.
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Sponsored Solutions Track II
Wednesday, 5 November
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Beyond Hadoop
Emerging Big Data Frameworks and Technology
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Scalable Microservice Architectures
This track addresses the ways companies with hundreds of fine-grained web-services (e.g. Netflix, LinkedIn) manage complexity!
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Java at the Cutting Edge
The latest and greatest in the Java ecosystem
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Engineering culture
Successes and failures in creating an engineering culture.
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Next gen HTML5 and JS
How Web Components, the Future of CSS, and more are changing the web.
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Sponsored Solutions Track III