A CITRIS Research Exchange Seminar with speaker Gerald Friedland.
TALK TITLE: “A Measurements-Based Approach to Machine Learning”
SPEAKER: Gerald Friedland, Adjunct Assistant Professor, Electrical Engineering and Computer Science, UC Berkeley
BIO: Gerald Friedland is teaching as adjunct faculty at the EECS and works on Machine Learning within CITRIS Health. He is also the founder and CTO of Brainome, Inc. Before that, he was a principal data scientist with Lawrence Livermore National Lab after being with the International Computer Science Institute for over 10 years. His work focuses on machine learning and multimedia signal processing. He was the lead figure behind the Multimedia Commons initiative, a collection of 100M images and 1M videos for research and has published more than 250 peer-reviewed articles in conferences, journals, and books. Friedland received his doctorate (summa cum laude) and master’s degree in computer science from Freie Universitaet Berlin, Germany, in 2002 and 2006, respectively.
ABSTRACT: Every field of science and engineering starts with measurements. When working on machine learning problems, modern data science often relies more on computation (let’s throw more GPUs at the problem) and guesswork (let’s see if we can modify Alexnet for our specific problem) than on any kind of measurements. In this talk, I will discuss an approach to supervised machine learning that is rooted in information-theoretic measurements. I will explain how this approach is especially interesting for high dimensional problems that, without measurements, suffer from “the curse of dimensionality”. I will explain the fundamentals of how measurements-based machine learning works, and also explore how the approach can be applied to solve real-world problems in CITRIS and beyond.
ABOUT THE SERIES: CITRIS Research Exchange delivers fresh perspectives on information technology and society from distinguished academic, industry, and civic leaders. Free and open to the public, this series highlights leading voices on societal-scale research issues. Each seminar takes place on Wednesdays from 12:00 pm to 1:00 pm PT. Have a suggestion for a great speaker? Please use this form to suggest potential speakers for our series.
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