My New Machine Learning Book on Stochastic Processes

My new book Stochastic Processes and Simulations is now published. 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 my GitHub repository.

Selected content:

• GPU clustering: Fractal supervised clustering in GPU (graphics processing unit) using image filtering techniques akin to neural networks, automated black-box detection of the number of clusters, unsupervised clustering in GPU using density (gray levels) equalizer.
• Inference: New test of independence, spatial processes, model fitting, dual confidence regions, minimum contrast estimation, oscillating estimators, mixture and surperimposed models, radial cluster processes, exponential-binomial distribution with infinitely many parameters, generalized logistic distribution.
• Nearest neighbors: Statistical distribution of distances and Rayleigh test, Weibull distribution, properties of nearest neighbor graphs, size distribution of connected components, geometric features, hexagonal lattices, coverage problems, simulations, model-free inference.
• Cool stuff: Random functions, random graphs, random permutations, chaotic convergence, perturbed Riemann Hypothesis (experimental number theory), attractor distributions in extreme value theory, central limit theorem for stochastic processes, numerical stability, optimum color palettes, cluster processes on the sphere.

The book is available here, on the new platform MachineLearningRecipes.com. There, you will find more information: extracts of the book, and access to the GitHub repository featuring the table of content, index, bibliography, list of exercises, and more. Also available on Amazon.