Fun Math Problems for Machine Learning Practitioners

This is part of a sequence that features the subsequent components of machine finding out:

• Mathematics, simulations, benchmarking algorithms based mostly totally on synthetic info (briefly, experimental info science)
• Opinions, for event regarding the value of a PhD in our space, or the utilization of some methods
• Methods, guidelines, tips of thumb, recipes, ideas
• Business analytics 
• Core Techniques 

This downside focuses on cool math points that embrace info items, provide code, and algorithms. Many have a statistical, probabilistic or experimental style, and some are dealing with dynamical packages. They could be utilized to extend your math knowledge, observe your machine finding out experience on genuine points, or for curiosity. My articles, posted on Data Science Central, are always written in simple English and accessible to professionals with often one 12 months of calculus or statistical teaching, on the undergraduate diploma. They are geared within the route of people who use info nonetheless are fascinating in gaining further smart analytical experience. The mannequin is compact, geared within the route of people who do not have an entire lot of free time. 

Despite these restrictions, state-of-the-art, of-the-beaten-path outcomes along with machine finding out commerce secrets and techniques and strategies and evaluation supplies are repeatedly shared. References to further superior literature (from myself and totally different authors) is equipped for those who must dig deeper throughout the issues talked about. 

1. Fun Math Problems for Machine Learning Practitioners

These articles give consideration to methods which have broad features or which is likely to be in every other case fundamental or seminal in nature.

1. Fascinating Facts About Complex Random Variables and the Riemann Hypothesis
2. More Surprising Math Images
3. Beautiful Mathematical Images
4. Deep visualizations to Help Solve Riemann’s Conjecture
5. Spectacular Visualization: The Eye of the Riemann Zeta Function
6. New Probabilistic Approach to Factoring Big Numbers
7. Simple Trick to Dramatically Improve Speed of Convergence
8. State-of-the-Art Statistical Science to Tackle Famous Number Theory Conjectures
9. New Perspective on Fermat’s Last Theorem
10. Fun Math: Infinite Nested Radicals of Random Variables – Connection with Fractals and Brownian Motions
11. Surprising Uses of Synthetic Random Data Sets
12. Two New Deep Conjectures in Probabilistic Number Theory
13. Extreme Events Modeling Using Continued Fractions
14. A Strange Family of Statistical Distributions
15. Some Fun with Gentle Chaos, the Golden Ratio, and Stochastic Number Theory
16. Fascinating New Results throughout the Theory of Randomness
17. From Infinite Matrices to New Integration Formula

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 latest perspective on the subject, along with tips of thumb and recipes which is likely to be easy to automate or mix in black-box packages, along with new model-free, data-driven foundations to statistical science and predictive analytics. The technique focuses on sturdy methods; it is bottom-up (from features to idea), in distinction to the conventional top-down technique.

  The supplies is accessible to practitioners with a one-year college-level publicity to statistics and likelihood. The compact and tutorial mannequin, that features many features 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 book is supposed for professionals in info science, laptop science, operations evaluation, statistics, machine finding out, enormous info, and arithmetic. In 100 pages, it covers many new issues, offering a latest perspective on the subject.

  It is accessible to practitioners with a two-year college-level publicity to statistics and likelihood. The compact and tutorial mannequin, that features many features (Blockchain, quantum algorithms, HPC, random amount expertise, 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, 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 based mostly and co-founded just some start-ups, along with one with a worthwhile exit (Data Science Central acquired by Tech Target). He recently opened Paris Restaurant, in Anacortes. You can entry Vincent’s articles and books, proper right here.