Reversible Markov Chains and Random Walks on Graphs

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.

This is an Introduction to Programming in Emacs Lisp, for people who are not programmers.

Higher-Order Perl is about functional programming techniques in Perl. It's about how to write functions that can modify and manufacture other functions.

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:

Lions' Commentary on UNIX 6th Edition, with Source Code by John Lions (1976) contains the complete source code of the 6th Edition Unix kernel plus a commentary. It is commonly referred to as the Lions book. Despite its age, it is still considered an excellent commentary on simple but high quality code.

Some great papers and a book about categories