Stochastic Processes and Simulations – A Machine Learning Perspective

This scratch course on stochastic processes covers significantly more material than usually found in traditional books or classes. The approach is original: Introduces a new yet intuitive type of random structure called perturbed lattice or Poisson-binomial process, as the gateway to all the stochastic processes. Such models have started to gain considerable momentum recently, especially in sensor data, cellular networks, chemistry, physics and engineering applications. Also presents state-of-the-art material in simple words, in a compact style, including new research developments and open problems and focuses on the methodology and principles, providing the reader with solid foundations and numerous resources: theory, applications, illustrations, statistical inference, references, glossary, educational spreadsheet, source code, stochastic simulations, original exercises, videos and more.

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. 
Topics covered include inference for spatial processes, GPU-based supervised learning, perturbed lattice point processes, new inference techniques such as dual confidence regions and Rayleigh test of independence,  experimental mathematics, simulations, and numerous resources: spreadsheets, data sets, code, videos, extensive bibliography, and original exercises with solutions.