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SUMMARY:Probabilistic Data Structures and Algorithms - Christian Steinruec
 ken (University of Cambridge)\, Alexandre Khae Wu Navarro (University of C
 ambridge)
DTSTART:20140123T150000Z
DTEND:20140123T163000Z
UID:TALK50482@talks.cam.ac.uk
CONTACT:Konstantina Palla
DESCRIPTION:Classic software engineering encourages us to think of a compu
 ter as a perfect machine that has an error probability of zero.  Software 
 components are typically designed to assume and provide exactly this type 
 of perfection.  Amazingly\, it is possible to construct powerful and effic
 ient algorithms by relaxing the zero error constraint: the demand for spac
 e and time resources can be drastically reduced in exchange for accepting 
 a small\, non-zero probability of error.\nThis RCC shows a variety of such
  techniques\, probabilistic data structures and algorithms\, and how they 
 can be used for machine learning on massive datasets.\n\nRequired reading:
  none.\nInstead\, please think about the following question: "What can be 
 gained from randomness? Can randomness ever help us solve deterministic pr
 oblems? (And if so\, how?)"\n
LOCATION:Engineering Department\, CBL Room 438
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