Name: Using Graph Algorithms for Improving Machine Learning Predictions
Start: 2019-04-15T18:30:00-04:00
End: 2019-04-15T20:30:00-04:00
Location: 805 21st St NW

To celebrate GlobalGraphCelebrationDay.com we are hosting an event to celebrate graphs and the birth of the inventor of graph theory!
Please RSVP here: https://neo4j.typeform.com/to/USb6It?event=datadc.
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Co-Hosted By:
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Data Science DC https://www.meetup.com/Data-Science-DC/
GraphDB DC https://www.meetup.com/graphdb-dc
Graph DB Baltimore https://www.meetup.com/graphdb-baltimore/
Washington DC AI & Deep Learning https://www.meetup.com/Washington-D-C-Artificial-Intelligence-Deep-Learning/

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TALK 1: Intro to Graph Theory
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What are graphs? Why do people use them? What does graph data look like? What are some common measures applied to graphs? Tommy will be giving a very brief introduction to graphs and graph theory.

**Bio:

Tommy Jones is a member of the technical staff at In-Q-Tel and a coordinator for Data Science DC. He holds an MS in mathematics and statistics from Georgetown University and a BA in economics from the College of William and Mary. He is a PhD student in the George Mason University Department of Computational and Data Sciences. He specializes in statistical models of language and time series modeling and is the author of the textmineR package for the R language. Tommy is also a Marine Corps veteran.

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TALK 2: Using Graph Algorithms for Improving Machine Learning Predictions
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Relationships are one of the most predictive indicators of behavior and preferences. One of the most practical ways to improve our machine learning predictions right away is by using graphs for connected features. In this session, we will start with an overview of which algorithms to apply for various features related to influence in a network, similarities, link prediction, and community detection.

You’ll learn how graph algorithms can provide more predictive features as well as aid in feature selection to reduce overfitting. We’ll also look at ways to improve machine learning efficiency such as graph filtering to avoids running ML across an entire dataset or having to manually pair data down.

**Bio:

Amy E. Hodler is a network science devotee and AI and Graph Analytics Program Manager at Neo4j. She promotes the use of graph analytics to reveal structures within real-world networks and predict dynamic behavior. Amy helps teams apply novel approaches to generate new opportunities at companies such as EDS, Microsoft, Hewlett-Packard (HP), Hitachi IoT, and Cray Inc. Amy has a love for science and art with a fascination for complexity studies and graph theory. She tweets @amyhodler.

Food will be kindly sponosored by Neo4j

Michael McKenzie

Neo4j

Graph Database Washington DC

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