Machine Learning, Statistics, Mathematics eBooks and Articles
Welcome to our digital library! You will find here eBooks, articles and tutorials covering original, off-the-beaten-path content in machine learning, operations research, statistics, dynamical systems, mathematics and related topics. Written by Vincent Granville PhD, the emphasis is on applications, the style is compact, and many illustrations are provided, including source code and Excel spreadsheets. Concepts are explained in simple English, avoiding jargon and arcane theories. Yet new, unpublished research-level material is presented to the readers interested in digging deeper.
Basins of attraction of the sine map, with smooth and chaotic areas
1. Our digital catalog
The prerequisite to read my books or articles is typically one year of calculus and/or probability at the undergraduate level. I focus on simple techniques, easy to understand and implement, yet very efficient with broad applications: for instance, model-free regression techniques or confidence intervals. It will appeal to engineers, physicists, mathematicians, MBA professionals, quants, software engineers and scientists interested in expanding their analytical horizons. Many original exercises included in most articles and eBooks are of interest both to college students and professors in Academia, offering a fresh and exciting perspective on the topics being discussed.I am in the process of creating these new eBooks and articles. Sign up for our newsletter (below, see section 2) to receive updates when the first few books are written. The topics covered will include the following.
Articles, booklets, and books
- Simulation: standard and non-standard probability distribution, cluster processes
- Original maths, stats and probability exercises
- Random number generation, encryption, and secure passwords
- Simple, overlooked, yet efficient statistical techniques
- Original introduction to discrete dynamical systems
- A catalog of beautiful mathematical images
- New perspectives on some famous mathematical conjectures
- Experimental machine learning on synthetic data
- The probabilistic method in number theory
- Off-the-beaten-path introduction to linear algebra
- Time series modeling: best kep secrets
- A plethora of unusual, little known probability distributions
- Free online platforms for mathematicians
2. Upcoming books
To not miss new material published regularly, sign up to receive monthly new book and article announcements from us, in your inbox. Your information is kept confidential and nor shared with third parties. Subscribers are entitled to future revisions of the eBooks and articles purchased from us, including second editions, for free.3. Free books and articles
The following eBooks are available for free to Data Science Central members:- Statistics: New Foundations, Toolbox, and Machine Learning Recipes
Available here. In about 300 pages and 28 chapters it covers many new topics, offering a fresh perspective on the subject, including rules of thumb and recipes that are easy to automate or integrate in black-box systems, as well as new model-free, data-driven foundations to statistical science and predictive analytics. The approach focuses on robust techniques; it is bottom-up (from applications to theory), in contrast to the traditional top-down approach.
The material is accessible to practitioners with a one-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications with numerous illustrations, is aimed at practitioners, researchers, and executives in various quantitative fields.
- Applied Stochastic Processes
Available here. Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems (104 pages, 16 chapters.) This book is intended for professionals in data science, computer science, operations research, statistics, machine learning, big data, and mathematics. In 100 pages, it covers many new topics, offering a fresh perspective on the subject.
It is accessible to practitioners with a two-year college-level exposure to statistics and probability. The compact and tutorial style, featuring many applications (Blockchain, quantum algorithms, HPC, random number generation, cryptography, Fintech, web crawling, statistical testing) with numerous illustrations, is aimed at practitioners, researchers and executives in various quantitative fields.
About the Author
Vincent Granville, PhD is a pioneering data scientist, mathematician, entrepreneur, investor, co-founder of Data Science Central (acquired in 2020), former VC-funded executive, author and patent owner. Vincent's past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, CNET, InfoSpace and other Internet startup companies (one acquired by Google). Vincent is also a former post-doct from 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. One of his books was published by Wiley. For details, see my Google Scholar profile, here.