Source: See article #5, in section 1
Part 2 of this short series focused on fundamental techniques, see here. In this Part 3, you will find several machine learning tricks and recipes, many with a statistical flavor. These are articles that I wrote in the last few years. 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
- Core Techniques
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. Machine Learning Tricks, Recipes and Statistical Models
These articles focus on techniques that have wide applications or that are otherwise fundamental or seminal in nature.
- Defining and Measuring Chaos in Data Sets: Why and How, in Simple W…
- Hurwitz-Riemann Zeta And Other Special Probability Distributions
- Maximum runs in Bernoulli trials: simulations and results
- Moving Averages: Natural Weights, Iterated Convolutions, and Centra…
- Amazing Things You Did Not Know You Could Do in Excel
- New Tests of Randomness and Independence for Sequences of Observations
- Interesting Application of the Poisson-Binomial Distribution
- Alternative to the Arithmetic, Geometric, and Harmonic Means
- Bernouilli Lattice Models – Connection to Poisson Processes
- Simulating Distributions with One-Line Formulas, even in Excel
- Simplified Logistic Regression
- Simple Trick to Normalize Correlations, R-squared, and so on
- Simple Trick to Remove Serial Correlation in Regression Models
- A Beautiful Result in Probability Theory
- Long-range Correlations in Time Series: Modeling, Testing, Case Study
- Difference Between Correlation and Regression in Statistics
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.
