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We've been taught that "data science" is the esoteric domain of PhDs,
but like anything else, it's easy once you understand it. This talk
explains the basics of data science, covering concepts in supervised
learning (including a detailed explanation of decision trees and
random forests) as well as examples of unsupervised learning
algorithms. Far from being a dry and academic topic, data science and machine learning are useful and practical analytical tools. (This talk is intended for a general audience.)
Topics will include:
1) An introduction to supervised learning using the popular decision
2) The concepts of training and scoring, and the meaning of "real time"
3) Model validation using holdout sets
4) Model complexity and overfitting; understanding bias and variance;
using ensembles to reduce variance
5) An overview of unsupervised learning models including clustering,
topic modeling and anomaly detection
6:30 pm to 7:00 pm Check In, Food, Networking
7:00 pm to 8:30 pm Presentation, Q & A
8:30 pm to 9:00 pm Networking
About the Speaker
David Gerster is Vice President of Data Science at BigML, an organization founded in 2010 "with the mission of making machine learning easy and beautiful for everyone". David's role is to promote the idea that data science is easy by speaking at conferences and teaching workshops. Since joining BigML in July 2013, he has spoken at CERN, Big Data Spain, Papis.io, DataLead (UC Berkeley), DataBeat (VentureBeat), and more than a dozen other events.
Prior to BigML, David held postions at Groupon and Yahoo. At Groupon, he built an elite data science team that trained the first machine-learned models for mobile deal relevance. At Yahoo, he led the project to collect billions of URL clickstreams in Hadoop and use them to improve Yahoo’s main web search algorithm.
David holds an MBA from the University of California at Berkeley and a bachelor’s degree from Harvard University.
http://linkedin.com/in/gerster ( https://www.linkedin.com/in/gerster ).