Practical Machine Learning: How To Decide What Really Matters

Big data is beginning to make a big difference when it comes to building machine learning systems. Moreover, many of the methods for building these systems don’t require years of study before you can apply them. In this talk, I will present examples of key principles that really matter in making choices about data, algorithms and architecture when you build practical learning systems.

From innovations in online recommendations to slicing an internet of things-based anomaly detection problem into approachable pieces, your ability to choose the right simplifcations is an important factor in getting good results. In addition to presenting key guidelines into how to make the right choices I will do a deep dive into a specific example: how to build a powerful but simple recommender with surprisingly easy implementation.

Meet Ted Dunning:

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Ted Dunning is Chief Applications Architect at MapR Technologies and committer and PMC member of the Apache Mahout, Apache ZooKeeper, and Apache Drill projects and mentor for Apache Storm. He contributed to Mahout clustering, classification and matrix decomposition algorithms and helped expand the new version of Mahout Math library. Ted was the chief architect behind the MusicMatch (now Yahoo Music) and Veoh recommendation systems, he built fraud detection systems for ID Analytics (LifeLock) and he has 24 issued patents to date. Ted has a PhD in computing science from University of Sheffield. When he’s not doing data science, he plays guitar and mandolin.

An Introduction to Practical Machine Learning: Puppies, Ponies and the Ideas Behind Recommendation

It's useful to take a look at the basic concepts that underlie machine learning, both as a way to better understand the project you are going to build and as a way to better communicate with other teams or organizations. Many of the big ideas are surprisingly accessible to grasp even to those who lack the technical expertise to execute them. I'll provide a quick look at how to find the key ideas around which successful machine learning projects are built.

Meet Ellen Friedman:

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Ellen Friedman is a committer for the open source Apache Mahout project and a contributer for the Apache Drill project. She works as a consultant in big data and scientific projects and writes about big data, including being co-author of the book Mahout in Action and the white paper Practical Machine Learning: Innovations in Recommendation. With a PhD in biochemistry, she has years of experience as a research scientist and has written about a variety of other technical topics including molecular biology, medicine and oceanography. Ellen is also co-author of a book of magic themed cartoons, A Rabbit Under the Hat.

Agenda:

6.00-6.45pm: Registration, Networking & book signage

6:45-7.00pm: Introduction

7:00-7:45pm: Talk by Ted Dunning & Ellen Friedman

7:45-8.15pm: Q&A session

In addition to inspiring presentations by Ted Dunning and Ellen Friedman, we will have a book signing session from 6 to 7pm. Make sure to be there on time, get your O'Reilly book 'Practical Machine Learning, Innovations in Recommendation' and have it signed by its authors Ted Dunning and Ellen Friedman.

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