What we're about

The LA Machine Learning Meetup began with events discussing a large variety of machine learning topics such as classification, clustering, neural networks, graphical algorithms, information retrieval, search, game theory, computational learning theory, reinforcement learning, collaborative filtering etc. More recently, a Data Science Track (http://www.meetup.com/LA-Machine-Learning/events/161322792/) was launched with the focus on business applications and the entire process of data mining (e.g. business understanding, data collection, exploratory data analysis, data transformations, feature engineering, modeling, model validation, deployment, communication of results). While in fact Machine Learning is a part of Data Science and not the other way around, rather than starting a new meetup group, Data Science is featured within the already existing Machine Learning meetup.

Upcoming events (1)

Bradley P. Allen @ ZEFR


Bradley Allen is the Chief Architect at Elsevier. In this meeting he will discuss using machine learning to understand scientific and medical articles. 6:30 PM Doors open 6:30 PM - 7:00 PM Snack and drinks served 7:00 PM - 8:00 PM Presentation and Q&A 8:00 PM - 8:30 PM Mingling 8:30 PM Doors close Title: From Content Publishing to Data Solutions via Machine Learning Abstract: Elsevier is the world's largest scientific and medical content publisher, with seven thousand employees globally and roughly three billion dollars in annual revenue. The scholarly communication ecosystem that Elsevier is part of has been radically transformed by the Internet and Web, and crazy though it may seem, this process is only just beginning. This talk will discuss the ways in which Elsevier is exploiting machine learning technology to effect this transition, by processing the scientific and medical literature together with the data exhaust associated with scientific research and authorship to create new data solutions for researchers and medical practitioners. We will touch on the impact of machine learning on scholarly communications, current work on assembling the talent and infrastructure to productionize machine learning applications, and specific applications within Elsevier's businesses. We will additionally place our efforts in the context of the latest upswing of interest in corporate artificial intelligence, and provide a personal perspective on what works, what doesn't, and what remains to be done in that area. Bio: Bradley P. Allen is an artificial intelligence expert and serial entrepreneur with over thirty years of experience bringing innovative software solutions to market. Currently, Brad is Chief Architect at Elsevier, where he leads the Architecture group within Elsevier’s technology organization, focusing on driving technology vision and roadmaps in collaboration with corporate strategy, working with development teams to build and evolve products and infrastructure, and guiding Elsevier Labs’ collaborative research into the future of scientific and medical publishing. Prior to Elsevier, Brad was founder and CTO at a series of enterprise software startups (Limbex, Trivida and Siderean) in the Los Angeles area, achieving successful exits in two of the three. He began his career in Los Angeles at Inference Corporation as one of the very first knowledge engineers. Brad is co-inventor on five US patents and has a BS in Applied Mathematics from Carnegie-Mellon University. Parking and directions: There is street parking in the neighborhood east of Redwood Ave. Please enter through the doors at 4101 Redwood Ave.

Past events (130)

Jesse Steinweg-Woods @ ZEFR


Photos (175)