Presented By O’Reilly and Intel AI
Put AI to Work
April 29-30, 2018: Training
April 30-May 2, 2018: Tutorials & Conference
New York, NY
Jorge Silva

Jorge Silva
Principal Machine Learning Developer, SAS

Jorge Silva is a principal machine learning developer at SAS. Previously, he was an adjunct professor at Instituto Superior de Engenharia de Lisboa (ISEL) and a senior research scientist at Duke University. His research interests include statistical models applied to large-scale problems, such as manifold learning, computer vision, and recommender systems. He holds multiple US patents and has authored numerous scholarly papers. Jorge holds a PhD in electrical and computer engineering from Instituto Superior Técnico (IST), Lisbon.

Sessions

4:50pm–5:30pm Wednesday, May 2, 2018
Online and active learning for recommender systems
Implementing AI, Models and Methods
 Location: Concourse A
Jorge Silva (SAS)
Average rating: ****.
(4.00, 2 ratings)
Recommender systems suffer from concept drift and scarcity of informative ratings. Jorge Silva explains how SAS uses a Bayesian approach to tackle both problems by making the learning process online and active. Active learning prioritizes the most informative users and items by quantifying uncertainty in a principled, probabilistic framework. Read more.