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Scaling Up Machine Learning: Parallel and Distributed Approaches
February 27, 2012 @ 6:30 pm
Scaling Up Machine Learning: Parallel and Distributed Approaches
Date
Monday, February 27, 2012 – 6:30pm – 8:10pm
Venue
LinkedIn
2025 Stierlin Ct.
Mountain View, CA 94043
See map: Google Maps
Speaker: Ron Bekkerman, LinkedIn
Event Details
In this talk, I’ll provide an extensive introduction to parallel and distributed machine learning. I’ll answer the questions “How actually big is the big data?”, “How much training data is enough?”, “What do we do if we don’t have enough training data?”, “What are platform choices for parallel learning?” etc. Over an example of k-means clustering, I’ll discuss pros and cons of machine learning in Pig, MPI, DryadLINQ, and CUDA. Time permitting, I’ll take a deep dive into parallel information-theoretic clustering.
Speaker Bio
Ron Bekkerman is a senior research scientist at LinkedIn where he develops machine learning and data mining algorithms to enhance LinkedIn products. Prior to LinkedIn, he was a researcher at HP Labs. Ron completed his PhD in Computer Science at the University of Massachusetts Amherst in 2007. He holds BSc and MSc degrees from the Technion—Israel Institute of Technology. Ron has published on various aspects of clustering, including multimodal clustering, semi-supervised clustering, interactive clustering, consensus clustering, one-class clustering, and clustering parallelization.
https://www.sfbayacm.org/event/scaling-machine-learning-parallel-and-distributed-approaches