More Fun Math Problems for Machine Learning Practitioners

This is part of a sequence that features the following options of machine learning:

• Mathematics, simulations, benchmarking algorithms based totally on synthetic data (briefly, experimental data science)
• Opinions, for event regarding the value of a PhD in our self-discipline, or the utilization of some strategies
• Methods, guidelines, pointers of thumb, recipes, suggestions
• Business analytics 
• Core Techniques 

This issue focuses on cool math points that embody data items, provide code, and algorithms. See earlier article proper right here. Many have a statistical, probabilistic or experimental style, and some are dealing with dynamical methods. They might be utilized to extend your math information, observe your machine learning experience on genuine points, or for curiosity. My articles, posted on Data Science Central, are always written in straightforward English and accessible to professionals with generally one yr of calculus or statistical teaching, on the undergraduate stage. They are geared within the path of people who use data nevertheless are fascinating in gaining additional smart analytical experience. The kind is compact, geared within the path of people who don’t want a wide range of free time. 

Despite these restrictions, state-of-the-art, of-the-beaten-path outcomes along with machine learning commerce secrets and techniques and methods and evaluation supplies are ceaselessly shared. References to additional superior literature (from myself and totally different authors) is obtainable for those who have to dig deeper inside the issues talked about. 

1. Fun Math Problems for Machine Learning Practitioners

These articles think about strategies which have broad functions or that are in another case elementary or seminal in nature.

1. New Mathematical Conjecture?
2. Cool Problems in Probabilistic Number Theory and Set Theory
3. Fractional Exponentials – Dataset to Benchmark Statistical Tests
4. Two Beautiful Mathematical Results – Part 2
5. Two Beautiful Mathematical Results
6. Four Interesting Math Problems
7. Number Theory: Nice Generalization of the Waring Conjecture
8. Fascinating Chaotic Sequences with Cool Applications
9. Representation of Numbers with Incredibly Fast Converging Fractions
10. Yet Another Interesting Math Problem – The Collatz Conjecture
11. Simple Proof of the Prime Number Theorem
12. Factoring Massive Numbers: Machine Learning Approach
13. Representation of Numbers as Infinite Products
14. A Beautiful Probability Theorem
15. Fascinating Facts and Conjectures about Primes and Other Special Nu…
16. Three Original Math and Proba Challenges, with Tutorial
17. Challenges of the week

2. Free books

• Statistics: New Foundations, Toolbox, and Machine Learning Recipes

  Available proper right here. In about 300 pages and 28 chapters it covers many new issues, offering a up to date perspective on the subject, along with pointers of thumb and recipes that are simple to automate or mix in black-box methods, along with new model-free, data-driven foundations to statistical science and predictive analytics. The technique focuses on sturdy strategies; it is bottom-up (from functions to concept), in distinction to the usual top-down technique.

  The supplies is accessible to practitioners with a one-year college-level publicity to statistics and likelihood. The compact and tutorial kind, that features many functions with fairly a number of illustrations, is aimed towards practitioners, researchers, and executives in quite a few quantitative fields.

• Applied Stochastic Processes

  Available proper right here. Full title: Applied Stochastic Processes, Chaos Modeling, and Probabilistic Properties of Numeration Systems (104 pages, 16 chapters.) This e-book is supposed for professionals in data science, laptop computer science, operations evaluation, statistics, machine learning, large data, and arithmetic. In 100 pages, it covers many new issues, offering a up to date perspective on the subject.

  It is accessible to practitioners with a two-year college-level publicity to statistics and likelihood. The compact and tutorial kind, that features many functions (Blockchain, quantum algorithms, HPC, random amount period, cryptography, Fintech, web crawling, statistical testing) with fairly a number of illustrations, is aimed towards practitioners, researchers and executives in quite a few quantitative fields.

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About the author:  Vincent Granville is a data science pioneer, mathematician, e-book author (Wiley), patent proprietor, former post-doc at Cambridge University, former VC-funded govt, with 20+ years of firm experience along with CNET, NBC, Visa, Wells Fargo, Microsoft, eBay. Vincent will also be self-publisher at DataShaping.com, and primarily based and co-founded plenty of start-ups, along with one with a worthwhile exit (Data Science Central acquired by Tech Target). He currently opened Paris Restaurant, in Anacortes. You can entry Vincent’s articles and books, proper right here.