Algorithms and methods of machine learning

Course: Data science and influences

Structural unit: Faculty of information Technology

Title
Algorithms and methods of machine learning
Code
ОК5
Module type
Обов’язкова дисципліна для ОП
Educational cycle
Second
Year of study when the component is delivered
2023/2024
Semester/trimester when the component is delivered
2 Semester
Number of ECTS credits allocated
4
Learning outcomes
Have specialized computer science problem-solving skills necessary for conducting research and carrying out innovative activities to develop new knowledge and procedures. Develop a conceptual model of an information or computer system. Develop and apply mathematical methods for the analysis of information models. Develop mathematical models and methods of data analysis (including large data). Test the software. Conduct research in the field of computer science. Collect, formalize, systematize and analyze the needs and requirements for the information or computer system being developed, operated or supported. To analyze the current state and global trends in the development of computer sciences and information technologies. Create and research informational and mathematical models of systems and processes under investigation, including automation objects.
Form of study
Distance form
Prerequisites and co-requisites
1) mastery of methods and models of intelligent data analysis when solving problems of processing large data sets; 2) mastery of modern programming languages and tools for designing programs for solving scientific and applied problems.
Course content
The discipline is devoted to the theoretical principles of the application of the machine learning apparatus, to the definition of the range of tasks that can be solved with the help of machine learning; the principles of formulating the formulation of problems arising in practical activities for their solution using machine learning methods, the principles of using machine learning methods to solve a wide class of science and technology problems. Dedicated to the use of methods, approaches and algorithms of machine learning in information and analytical activities; competent choice of the best machine learning method suitable for a specific task; building machine learning models.
Recommended or required reading and other learning resources/tools
Planned learning activities and teaching methods
Lectures, practical activities, individual work, consultations
Assessment methods and criteria
The credit is issued to the student based on the results of work during the semester. At the last practical lesson, a final modular test is conducted in the form of a final computer test with a final grade of up to 20 points. Upon receipt of the resulting final number of points from 60 and above, the student is assigned a credit. If the student wishes to improve his result if he has credit points, he has the right to take a credit for which 20 points are awarded, but the total number of points cannot exceed 100 points. Students who scored a total of fewer points than the critical calculated minimum - 36 points are not allowed to take the test. The recommended minimum for admission to the test is 48 points.
Language of instruction
English

Lecturers

This discipline is taught by the following teachers

Departments

The following departments are involved in teaching the above discipline