Probabilistic Programming and Bayesian Methods for Hackers
by Cameron Davidson-Pilon
Publisher: GitHub, Inc. 2013
This book is designed as an introduction to Bayesian inference from a computational understanding-first, and mathematics-second, point of view. The book assumes no prior knowledge of Bayesian inference nor probabilistic programming.
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by Leonard Soicher, Franco Vivaldi - Queen Mary University of London
This text is a course in mathematical algorithms, intended for second year mathematics students. It introduces the algorithms for computing with integers, polynomials and vector spaces. The course requires no computing experience.
by Allen B. Downey - Green Tea Press
Think Stats is an introduction to Probability and Statistics for Python programmers. This new book emphasizes simple techniques you can use to explore real data sets and answer interesting statistical questions. Basic skills in Python are assumed.
by Bhubaneswar Mishra - Courant Institute of Mathematical Sciences
The main purpose of the book is to acquaint advanced undergraduate and graduate students in computer science, engineering and mathematics with the algorithmic ideas in computer algebra so that they could do research in computational algebra.
by Richard D. Jenks, Robert S. Sutor - axiom-developer.org
Axiom is a free general purpose computer algebra system. The book gives a technical introduction to AXIOM, interacts with the system's tutorial, accesses algorithms developed by the symbolic computation community, and presents advanced techniques.