inference 469
Information Theory, Inference, and Learning Algorithms by David J.C. MacKay
16 days ago by dhartunian
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.
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information-theory
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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.
16 days ago by dhartunian
David MacKay: Information Theory, Inference, and Learning Algorithms: The Book
6 weeks ago by chris_johnsen
[Recommended][HR-r] as containing a good introduction to Bayesian statistics.
[HN-r]: http://news.ycombinator.com/item?id=3793948
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[HN-r]: http://news.ycombinator.com/item?id=3793948
6 weeks ago by chris_johnsen
Economics now = Freudian psychology in the 1950s: More on the incoherence of “economics exceptionalism” « Statistical Modeling, Causal Inference, and Social Science
9 weeks ago by ewout
from Statistical Modeling, Causal Inference, and Social Science http://andrewgelman.com
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9 weeks ago by ewout
Factual – a new place to find data « Statistical Modeling, Causal Inference, and Social Science
february 2012 by ewancarr
from Statistical Modeling, Causal Inference, and Social Science http://andrewgelman.com
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february 2012 by ewancarr
Rust: A Safe, Concurrent, Practical Language
february 2012 by dale.hagglund
Rust is a curly-brace, block-structured expression language. It visually resembles the C language family, but differs significantly in syntactic and semantic details. Its design is oriented toward concerns of “programming in the large”, that is, of creating and maintaining boundaries – both abstract and operational – that preserve large-system integrity, availability and concurrency
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from delicious
february 2012 by dale.hagglund
David MacKay: Information Theory, Inference, and Learning Algorithms: Home
january 2012 by seanjtaylor
His information theory+inference book is a slow read but has so many ideas. Exercises are unconventional and very deep
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january 2012 by seanjtaylor
Advice on do-it-yourself stats education? « Statistical Modeling, Causal Inference, and Social Science
january 2012 by wrrn
from Statistical Modeling, Causal Inference, and Social Science http://andrewgelman.com
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january 2012 by wrrn
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