This book is aimed at senior undergraduates and graduate students in Engi-
neering, Science, Mathematics, and Computing. It expects familiarity with
calculus, probability theory, and linear algebra as taught in a rst- or second-
year undergraduate course on mathematics for scientists and engineers.
Conventional courses on information theory cover not only the beauti-
ful theoretical ideas of Shannon, but also practical solutions to communica-
tion problems. This book goes further, bringing in Bayesian data modelling,
Monte Carlo methods, variational methods, clustering algorithms, and neural
networks.
Why unify information theory and machine learning? Because they are
two sides of the same coin. In the 1960s, a single eld, cybernetics, was
populated by information theorists, computer scientists, and neuroscientists,
all studying common problems. Information theory and machine learning still
belong together. Brains are the ultimate compression and communication
systems. And the state-of-the-art algorithms for both data compression and
error-correcting codes use the same tools as machine learning.

In computer science, denormal numbers or denormalized numbers (now often called subnormal numbers) fill the underflow gap around zero in floating point arithmetic: any non-zero number which is smaller than the smallest normal number is 'sub-normal'.

In this article I digested a number of MapReduce patterns and algorithms to give a systematic view of the different techniques that can be found in the web or scientific articles. Several practical case studies are also provided. All descriptions and code snippets use the standard Hadoop’s MapReduce model with Mappers, Reduces, Combiners, Partitioners, and sorting.

Data structures play a central role in modern computer science. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). In addition, data structures are essential building blocks in obtaining efficient algorithms. This course covers major results and current directions of research in data structures:

Textbook. The textbook Algorithms, 4th Edition by Robert Sedgewick and Kevin Wayne surveys the most important algorithms and data structures in use today. The textbook is organized into six chapters:

PROBLEM: You are a web programmer. You have users. Your users rate stuff on your site. You want to put the highest-rated stuff at the top and lowest-rated at the bottom. You need some sort of "score" to sort by.

The PCG Wiki is a central knowledge-base for everything related to Procedural Content Generation, as well as a detailed directory of games using Procedural Content Generation.

This is a dictionary of algorithms, algorithmic techniques, data structures, archetypal problems, and related definitions. Algorithms include common functions, such as Ackermann's function. Problems include traveling salesman and Byzantine generals. Some entries have links to implementations and more information. Index pages list entries by area and by type. The two-level index has a total download 1/20 as big as this page.

His "home page" at http://okmij.org/ftp/ is a tremendously valuable collection of code, papers and archived UseNet posts on the HaskellLanguage, on the SchemeLanguage and Macros (he shows "How to write seemingly unhygienic and referentially opaque macros with syntax-rules", i.e. he writes unhygienic macros using the hygienic DefineSyntax macro system), OnMonads, on types and type systems (see TypeArithmeticsPaper), on LambdaCalculus and ChurchNumerals etc. Then there's the specification of SXML (an embedding of XML data into Scheme), a scheme implementation of a PurelyFunctionalObjectOrientedSystem?, CeePlusPlus libraries for numerical mathematics and image processing etc.
--from http://c2.com/cgi/wiki?OlegKiselyov