Solving Problems with Machine Learning: Identifying Fake News Through Natural Language Processing

2:40 - 3:10 | Thursday, May 23

In this talk we explore real world use case applications for automated “Fake News” evaluation using contemporary deep learning article vectorization and tagging. We begin with the use case and an evaluation of the appropriate context applications for various deep learning applications in fake news evaluation. Technical material will review several methodologies for article vectorization with classification pipelines, ranging from traditional to advanced deep architecture techniques. We close with a discussion on troubleshooting and performance optimization when consolidating and evaluating these various techniques on active data sets.

Mike Tamir
Chief Scientist and Head of Machine Learning; Data Science Faculty, Susquehanna International Group (SIG); UC Berkeley

Mike serves as Chief Scientist and Head of Machine Learning for SIG, UC Berkeley Data Science faculty, and Director of Phronesis ML Labs. He has led teams of Data Scientists in the bay area as Head of Data Science at Uber ATG, Chief Data Scientist for InterTrust and Takt, Director of Data Sciences for MetaScale/Sears, and CSO for Galvanize where he founded the galvanizeU-UNH accredited Masters in Data Science degree and oversaw the company’s transformation from co-working space to Data Science organization.