Welcome to Machine Learning 101! We will be learning the basics of machine learning by building real applications: recommender and image analysis with deep learning.
The tutorial landing page (https://dato.com/events/training/2015-strata-nyc.html) contains the agenda for the day as well as relevant material. Please follow the instructions for setting up your laptop PRIOR to arriving onsite.
If you run into any problems during installation, please email firstname.lastname@example.org.
This hands-on tutorial provides a quick start to building intelligent business applications using machine learning. Learn about machine learning basics, feature engineering, recommender systems, and deep learning. We will build and deploy large-scale machine learning applications with Dato’s Machine Learning platform: GraphLab Create, Dato Distributed, and Dato Predictive Services. The program will center around building two applications: a content-based recommender that tells you which talks you might be interested in at Strata, and an image search application built using deep learning.
We will walk you through all the steps of prototyping and production: data cleaning, feature engineering, model building and evaluation, and deployment.
Please check back here prior to the tutorial date for installation instructions.
Alice Zheng is a senior manager of applied science on the machine learning optimization team on Amazon’s advertising platform. She specializes in research and development of machine learning methods, tools, and applications. She’s the author of Feature Engineering for Machine Learning. Previously, Alice has worked at GraphLab, Dato, and Turi, where she led the machine learning toolkits team and spearheaded user outreach; was a researcher in the Machine Learning Group at Microsoft Research – Redmond. Alice holds PhD and BA degrees in computer science and a BA in mathematics, all from UC Berkeley.
Chris DuBois is a data scientist focused on building tools for other data scientists. At Dato, Chris has helped design and implement tools for creating recommendation systems and for large-scale text analysis. His current work makes it simpler to train models that generalize well. After studying applied mathematics at Pomona College, he earned a PhD in statistics from the University of California, Irvine, where he researched latent variable models for social-network data occurring over time.
Piotr Teterwak works on the toolkit development team at Dato. He received a BA in computer science from Dartmouth College, where he conducted work exploring the learning of convolutional deep neural nets with applications in computer vision.
Krishna Sridhar is a data scientist at Dato. He holds a PhD in computer science from the University of Wisconsin-Madison, where he worked on high-performance software for large-scale problems in mathematical optimization and data analysis. Krishna’s work has been used in applications such as healthcare, industrial production planning, and machine learning.
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