Abstract
SHS investigation development is considered from the geographical and historical viewpoint. 3 stages are described. Within Stage 1 the work was carried out in the Department of the Institute of Chemical Physics in Chernogolovka where the scientific discovery had been made. At Stage 2 the interest to SHS arose in different cities and towns of the former USSR. Within Stage 3 SHS entered the international scene. Now SHS processes and products are being studied in more than 50 countries.
Abstract
Mathematics is the rock-solid foundation of everything that happens when
science is present, and it is also extremely important in the field of data science since
mathematical ideas assist discover models and facilitate the development of algorithms.
But, the concepts they present and the tools they enable are the only reasons statistics
and arithmetic are so crucial to data science. There is a particular type of mathematical
reasoning that is necessary to grasp data, beyond the fundamentals of calculus, discrete
mathematics, and linear algebra. For the implementation of such algorithms in data
science, a thorough understanding of the various principles of probability and statistics
is essential. Machine learning is one of the many modern data science techniques that
has a strong mathematical base. The evidence presented in this chapter backs up our
earlier claim that math and statistics are the fields that offer the greatest tools and
approaches for extracting structure from data. For newcomers coming from other
professions to data science, math proficiency is crucial.
Keywords:
Applications in medical science, Bayes’ theorem, Binomial, Bernoulli, Computer vision, Calculus, Calculus in machine learning, Gaussian normal, Linear algebra, Loss function, Mean squared error, Mean absolute error, Nonparametric statistical methods, Regression.
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Authors:Bentham Science Books