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SUMMARY:Applied Probabilistic Algorithms for Big Data Analysis - Advait Sa
 rkar (University of Cambridge)
DTSTART:20151103T130000Z
DTEND:20151103T140000Z
UID:TALK60534@talks.cam.ac.uk
CONTACT:Heidi Howard
DESCRIPTION:Introductory algorithms courses encourage us to think of compu
 ters as perfect machines that calculate exact answers. We typically design
  programs to provide exactly this type of perfection. However\, it is poss
 ible to construct efficient algorithms by relaxing the zero error constrai
 nt. The demand for space and time resources can be drastically reduced in 
 exchange of a small\, quantifiable probability of error.\n\nIn this lectur
 e\, we will follow the journey of MildlyInappropriateCatAppreciationSociet
 y.com and its competitors as they try to tackle some of the problems of ma
 naging large amounts of cat-related data. Motivated by examples and terrib
 le cat puns\, you will learn 5 probabilistic techniques that allow you do 
 things such as:\n* efficiently test whether an item is already present in 
 a gigantic distributed database\n* efficiently count the number of distinc
 t items in said big database\n* efficiently tabulate the frequencies of di
 fferent items in said big database\n\nYou will learn these techniques and 
 their error bounds in sufficient detail that you will be able to implement
  them once the lecture is finished. They can all be implemented in a few d
 ozen lines of code!
LOCATION:Computer Laboratory\, William Gates Building\, Room SW01
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