Source: see here
Part 1 of this short series focused on the business analytics / BI / operational research aspects, see here. In this Part 2, you will find the most interesting machine learning and statistics articles that I wrote in the last few years, focusing on core technical aspects. The whole series will feature articles related to the following aspects of machine learning:
- Mathematics, simulations, benchmarking algorithms based on synthetic data (in short, experimental data science)
- Opinions, for instance about the value of a PhD in our field, or the use of some techniques
- Methods, principles, rules of thumb, recipes, tricks
- Business analytics (Part 1)
My articles are always written in simple English and accessible to professionals with typically one year of calculus or statistical training, at the undergraduate level. They are geared towards people who use data but are interesting in gaining more practical analytical experience. Managers and decision makers are part of my intended audience. The style is compact, geared towards people who do not have a lot of free time.
Despite these restrictions, state-of-the-art, of-the-beaten-path results as well as machine learning trade secrets and research material are frequently shared. References to more advanced literature (from myself and other authors) is provided for those who want to dig deeper in the interested topics discussed.
1. Core techniques
These articles focus on techniques that have wide applications or that are otherwise fundamental or seminal in nature.
- Introducing an All-purpose, Robust, Fast, Simple Non-linear Regression
- Variance, Attractors and Behavior of Chaotic Statistical Systems
- New Family of Generalized Gaussian Distributions
- Gentle Approach to Linear Algebra, with Machine Learning Applications
- Confidence Intervals Without Pain
- Re-sampling: Amazing Results and Applications
- How to Automatically Determine the Number of Clusters in your Data – and more
- New Perspectives on Statistical Distributions and Deep Learning
- A Plethora of Original, Not Well-Known Statistical Tests
- New Decimal Systems – Great Sandbox for Data Scientists and Mathema…
- Are the Digits of Pi Truly Random?
- Data Science and Machine Learning Without Mathematics
- Advanced Machine Learning with Basic Excel
- State-of-the-Art Machine Learning Automation with HDT
- Tutorial: Neutralizing Outliers in Any Dimension
- The Fundamental Statistics Theorem Revisited
- Variance, Clustering, and Density Estimation Revisited
- The Death of the Statistical Tests of Hypotheses
- 4 Easy Steps to Structure Highly Unstructured Big Data, via Automat…
- The best kept secret about linear and logistic regression
- Black-box Confidence Intervals: Excel and Perl Implementation
- Jackknife and linear regression in Excel: implementation and compar…
- Jackknife logistic and linear regression for clustering and predict…
2. Free books
- 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.
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About the author: Vincent Granville is a data science pioneer, mathematician, book author (Wiley), patent owner, former post-doc at Cambridge University, former VC-funded executive, with 20+ years of corporate experience including CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent is also self-publisher at DataShaping.com, and founded and co-founded a few start-ups, including one with a successful exit (Data Science Central acquired by Tech Target). You can access Vincent’s articles and books, here.