eBook: Stochastic Processes and Simulations – A Machine Learning Perspective


The 100 page book on stochastic processes. Published in 2022. This off-the-beaten-path machine learning tutorial is designed for busy professionals, researchers and students eager to learn and apply methods ranging from simple to advanced, in a minimum amount of time. Offered with data sets, source code, videos, spreadsheets and solved exercises. See full description below.


By Vincent Granville, published in 2022. PDF format, 96 pages. ISBN: 978-0578384061. Volume 1, version 6.0 with Python code.

Written for machine learning practitioners, software engineers and other analytic professionals interested in expanding their toolset and mastering the art. Discover state-of-the-art techniques explained in simple English, applicable to many modern problems, especially related to spatial processes and pattern recognition. This textbook includes numerous visualization techniques (for instance, data animations using video libraries in R), a true test of independence, simple illustration of dual confidence regions (more intuitive than the classic version), minimum contrast estimation (a simple generic estimation technique encompassing maximum likelihood), model fitting techniques, and much more. The scope of the material extends far beyond stochastic processes.

The textbook is easy to navigate and full of clickable links. A comprehensive index, large bibliography and glossary with backlinks makes it a compact reference on the subject. This modern PDF document has been designed (both in terms of presentation and content) to meet the highest standards. Accompanying data sets, source code, Excel spreadsheets and videos are available on GitHub.

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