IMPLEMENTATION OF PROFESSIONALLY APPLIED ORIENTATION OF PROBABILISTIC METHODS IN THE TRAINING OF TECHNICAL SPECIALTIES

Abstract

This article discusses the application of the formula of total
probability and Bayes’ formula for solving applied problems related to technical
specialties. Probabilistic methods are widely used in various areas of information
technology, such as data processing, machine learning, cybersecurity, and
data analysis. The total probability formula allows for the consideration of all
possible scenarios that may affect the outcome of a task, while Bayes’ formula
offers a method for updating probabilities based on new information. The article
provides examples of applying these methods to solve problems of classification,
forecasting, and decision-making under uncertainty. In particular, it addresses
issues of predicting user behavior, assessing risks in information systems,
and optimizing machine learning algorithms. The use of the total probability
formula and Bayes’ formula contributes to improving the accuracy of models
and enhancing the efficiency of algorithms. It is shown that these methods are
especially useful in applied technical tasks, where decisions must be made under
conditions of incomplete or inaccurate information. The article emphasizes
the importance of applying probabilistic approaches in the development of
modern solutions in information technology. During the research, the results of a
pedagogical experiment conducted at the Almaty University of Power Engineering
and Telecommunications show a positive impact of applying the full probability
formula and Bayes’ formula on the knowledge level of students in the IT field.
The results demonstrate that the performance of the experimental group is higher
compared to that of traditional teaching methods. Therefore, probabilistic methods
are effective tools for solving complex problems in information technology and
contribute to the development of analytical thinking among professionals.