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http://www.cs.princeton.edu/courses/archive/spr09/cos333/beautiful.html
http://www.cs.princeton.edu/courses/archive/spr09/cos333/beautiful.htmlA Regular Expression Matcher: "smaller and more powerful than I had thought possible" http://t.co/f1hxsr5 #beautifulcode – Andrew Gerrand (enneff) http://twitter.com/enneff/status/81202894266834944
Tags: c, regex, programming, code, regexp, unix, beautiful, development, grep, matching Saved by: admin
COS 493, Spring 2002: Schedule and Readings
The course deals with algorithmic tools and techniques for the organization, manipulation and processing of large amounts of data. With the current information explosion, several applications require the manipulation of very large data sets that exceed the size of the main memory of a typical processor. The amount of data is so large that we may only make a few passes over it, or cannot examine all of it. Certain problems that arise in this setting have been studied classically, e.g. How do we compress large files ? How do we deal with bit errors that are inevitable in the storage and transmission of large amounts of data? Recent research has examined many of the newer issues that arise in this context, e.g. Can we design very efficient sublinear algorithms for interesting computational tasks? What can be computed in a single pass over the data? How can we deal with high dimensional data efficiently? How do we deal with web data, e.g. assess the relative importance of web pages?
http://www.cs.princeton.edu/courses/archive/spring02/cs493/schedule.html
Tags: algorithms, data, algorithm, datamining, programming, cs, machinelearning, lecture, ai, statistics Saved by: admin
The course deals with algorithmic tools and techniques for the organization, manipulation and processing of large amounts of data. With the current information explosion, several applications require the manipulation of very large data sets that exceed the size of the main memory of a typical processor. The amount of data is so large that we may only make a few passes over it, or cannot examine all of it. Certain problems that arise in this setting have been studied classically, e.g. How do we compress large files ? How do we deal with bit errors that are inevitable in the storage and transmission of large amounts of data? Recent research has examined many of the newer issues that arise in this context, e.g. Can we design very efficient sublinear algorithms for interesting computational tasks? What can be computed in a single pass over the data? How can we deal with high dimensional data efficiently? How do we deal with web data, e.g. assess the relative importance of web pages?
http://www.cs.princeton.edu/courses/archive/spring02/cs493/schedule.html
Tags: algorithms, data, algorithm, datamining, programming, cs, machinelearning, lecture, ai, statistics Saved by: admin