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, here. For a full description including table of contents, see here.
About the Author
Vincent Granville is a pioneering data scientist and machine learning expert, co-founder of Data Science Central (acquired by TechTarget in 2020), former VC-funded executive, author and patent owner. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace. Vincent is also a former post-doc at Cambridge University, and the National Institute of Statistical Sciences (NISS).
Vincent published in Journal of Number Theory, Journal of the Royal Statistical Society (Series B), and IEEE Transactions on Pattern Analysis and Machine Intelligence. He is also the author of multiple books, available here. He lives in Washington state, and enjoys doing research on stochastic processes, dynamical systems, experimental math and probabilistic number theory.
There are no reviews yet.