Note: This meeting has been cancelled due to dangerously unhealthy air quality in the Bay Area.
Full details about this meeting will be posted here: http://compdatascience.org/entropy.
The next meeting is scheduled on November 30.
The 'Information and Uncertainty in Data Science' Discussion Group is a forum for open inquiry and discussion about a wide range of recurring data science fundamentals, including information, uncertainty, entropy, bits, probability, machine learning, generalization, and others. The group facilitates academic discourse on the practical use of the fundamental concepts across a wide variety of research disciplines, and strives for clarity and understanding using real-world scenarios, visual examples, cutting edge questions and unique perspectives. This group focusses on understanding and sharing concepts that are often buried in mathematical language, especially entropy, reduction of uncertainty and connections between physical systems and information systems. All interested members of the UC Berkeley, UCSF, LBL and LLNL communities are welcome and encouraged to attend. More details available at http://compdatascience.org/entropy. Contact: BIDS Senior Fellow Gerald Friedland.
Dr. Gerald Friedland is research scientist with Lawrence Livermore National Laboratory, and he is also teaching as an adjunct professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. His work focusses on large-scale machine learning for multimedia retrieval, and he has also worked in privacy and privacy education. He has published more than 200 peer-reviewed articles in conferences, journals, and books and also co-authored the textbook Multimedia Computing. Dr. Friedland received his doctorate in computer science from Freie Universitaet Berlin, Germany.