I am excited to announce our May meetup. We are going to look at Machine Learning on Big Data. Alex Gray, co-founder of SkyTree.net will be our speaker.
6pm - 6:30pm : enjoy food + drinks and network
6:30pm - 7:30pm : talk : Machine Learning on Big Data by Alex Gray
7:30pm - 8pm : more food & networking
This event is generously hosted by Samsung! (Yummy food & drinks !!)
The event FREE to attend.
Real-World Machine Learning on Big Data: Which Method(s) Should You Use?
Suppose you have a real-world big data problem before you, and you want to use machine learning (ML) to solve it. Which ML method(s) should you use? How does the fact that the dataset is big affect your choices? Drawing on two decades of experience in ML on big data, I will highlight a few key principles that can be distilled from the thousands of theoretical and experimental results in the research literature surrounding such questions. These will be illustrated through a handful of real-world ML success stories, where best-in-class results were achieved, including difficult examples in medical diagnosis, direct marketing, financial services, and astronomy.
About Speaker -- Alex Gray
Dr. Gray obtained degrees in Applied Mathematics and Computer Science from Berkeley and a PhD in Computer Science from Carnegie Mellon, and is a tenured professor at Georgia Tech. His lab works to scale up all of the major practical methods of machine learning (ML) to massive datasets. He began working on this problem at NASA in 1993 (long before the current fashionable talk of “big data”). His large-scale algorithms helped enable the Science journal’s Top Breakthrough of 2003, and have won a number of research awards. He is a member of the National Academy of Sciences (NAS) Committee on the Analysis of Massive Data, is a NAS Kavli Scholar, and frequently gives invited tutorial lectures on massive-scale ML at top research conferences and agencies. Dr. Gray is currently CTO and Co-Founder of “Skytree, Inc.—the Machine Learning Company” based in San Jose.
See you all there!