| Module | Subject # | ECTS credits | Content | Objectives | Learning outcomes | |
|---|---|---|---|---|---|---|
| Introduction to Cloud Computing | AIN1-042 | 5 | Introduction to cloud computing. Platforms for cloud computing. Parallel programming in the cloud. Distributed storage systems. Virtualisation. Cloud computing security. Multicore operating systems | Formation of students' theoretical knowledge and practical skills on cloud computing | EDO1 - The student knows cloud computing platforms, advantages and disadvantages of different cloud computing platforms. EDO2 - The student is able to analyse the trade-offs between deploying applications in the cloud and on-premises infrastructure, deploying applications in commercial cloud computing infrastructures such as Amazon Web Services, Windows Azure and Google AppEngine. EDO3 - The student is able to program data-intensive parallel applications in the cloud. | |
| Privacy decision support | AIN1-043 | 5 | Decision support system. Structure of a decision support system . Features of a decision support system. Applications of decision support. Comparative approval decision support systems. Comparative privacy decision support systems | To form students' theoretical knowledge and practical skills in the use of various decision support tools for privacy purposes | EDO1 - The student knows behavioural and normative theories of decision making, the value of decision support systems for individuals and organisations. EDO2 - The student is able to design decision support systems and processes. EDO3 - The student is able to acquire new knowledge in the area of privacy decision support | |
| Introduction to Big Data | AIN1-044 | 5 | The concept of Big Data. Features of collecting, storing, processing and analysing large data sets. Sources of big data. The use of big data in science, business, public administration. Options for building distributed databases, replication, fragmentation. Consistency. CAP-theorem. Classes of NoSQL databases. Examples of NoSQL databases. Column families. Graph DBMSs. Data consolidation tasks. Multidimensional data warehouses. Relational data warehouses. Virtual storage. Fuzzy environments. Introduction to ETL. Data transformation in ETL. Data enrichment. Map- Reduce technology. GOOGLE BIGTABLE. Full-text search. Parallel queries. Principles of data analysis. Structured data. Preparation of data for analysis. KDD and Data Mining techniques Introduction to data transformation. Transformation of organised data. Grouping data. Data fusion. Quantisation. Normalisation and coding of data. Principles of data analysis. Structured data. Preparation of data for analysis. KDD and Data Mining techniques. Introduction to data visualisation. General purpose visualisers. OLAP-analysis. Visualisers for model quality assessment. Visualisers for model interpretation. Identification of patterns in the form of decision trees, logical rules, neural networks. Review of modern data analysis software: Statistica, SPSS, Excel, R-Studio, KERAS. Means of building distributed information systems for BigData. Methods of data analysis | Formation of students' professional competences in the field of development and use of big data. | EDO1 - The student knows the methods of decision-making based on big data. EDO2 - The student is able to apply data analysis methods for decision making. EDO3 - The student is able to work in a team, independently acquire and utilise new knowledge and skills in the introduction of big data | |
| Basics of Scientific Research | AIN1-045 | 5 | Creativity in scientific and design work. Overview of methods of technical creativity. Methods of scientific research in engineering. Classification of research methods. Information and patent search. Setting up an experiment. Systematisation of information. Planning of research and development. Mathematical processing of experimental results. Formalisation of R&D results. Formalisation of the report on research work | Formation of students' theoretical knowledge in the field of the current state, directions of development and performance of scientific research in the field of profile orientation. | EDO1 - The student knows the basic logical methods and techniques of scientific research, methodological theories and principles of modern science, the basis of modern computer technologies, criteria of dependence of features and homogeneity of data, criteria of significance of parameters. EDO2 - The student is able to carry out logical and methodological analysis of scientific research and its results, apply mathematical methods in technical applications, carry out a patent search, plan a scientific experiment, give a public speech with argumentation, conduct discussions and polemics. EDO3 - The student is able to carry out methodological justification of scientific research, evaluate the effectiveness of scientific activity, use network technologies and multimedia in education and science; choose criterion parameters depending on the requirements for product quality and production costs, formulate a research problem based on production needs, identify distribution functions, justify criterion parameters. | |
| Number Theory | AIN1-046 | 5 | Mastering of methods of investigation and solution of equations in integers, properties of prime and composite numbers, laws of distribution of prime numbers in natural series and arithmetic progressions. | Mastering of methods of investigation and solution of equations in integers, properties of prime and composite numbers, laws of distribution of prime numbers in natural series and arithmetic progressions. | EDO1 - The student knows the properties of prime and composite numbers, the laws of distribution of prime numbers in the natural series, the properties of rings of subtraction classes in natural modules, the basic properties of algebraic extensions of the field of rational numbers and finite fields, the properties of arithmetic functions. EDO2 - The student is able to solve linear and quadratic equations from several variables, systems of linear equations in integers, as well as establish solvability and find solutions to algebraic comparisons and systems of comparisons, demonstrative comparisons, find systems of first roots, construct rational approximations to real numbers. EDO3 -The student is able to independently acquire and use new knowledge and skills in number theories. | |
| Machine Learning | AIN1-047 | 5 | Motivation. Manipulation of data. Visual analytics. Clustering. Regression. Classification. Deep learning | To form students' knowledge of the conceptual foundations of machine learning and practical skills in working with tools, models and methods of machine learning. | EDO1 - The student knows the key concepts, goals and objectives of using machine learning; methodological foundations of applying machine learning algorithms. EDO2 - The student is able to apply skills in visualising the results of machine learning algorithms, and interpreting the results. EDO3 - The student is able to work in a team, independently acquire and utilise new knowledge and skills in machine learning. | |
| Privacy and Ethics | AIN1-048 | 5 | Ethics and information technology. Information Integrity. Plagiarism in the online environment. Identity in cyberspace. Avatars and anonymity. Privacy. Synthesis. The ethics of gaming. Cheating in games. Virtual environments and ethics | To form students' theoretical knowledge in the field of ethical issues and privacy related to new information technologies | EDO1 - The student knows modern models of information ethics, ethical standards in relation to new technologies, legal and ethical aspects related to security and privacy of information technologies. EDO2 - The student is able to apply ethical standards to interpret personal and group behaviour when using various information technology tools. EDO3 - The student is able to acquire new knowledge using modern educational and information technology in the area of ethical issues and privacy. | |
| Robotics | AIN1-049 | 5 | Introduction to the concept of robotics. Microcontrollers and microprocessors. Sensors and their types. Actuating devices. Fundamentals of electronics. Microcontroller programming. Studying Arduino IDE. Studying Tinkercad. Working with LEDs, creation of traffic lights. Working with potentiometer, controlling the brightness of LED. Working with a photoresistor, creating automatic lighting. Work with buttons. Work with a servo motor, creating a manipulator. Work with a geared motor, creation of a machine with remote control | Formation of students' theoretical knowledge and practical skills in robotics. | EDO1 - The student knows the theoretical basis of sensors, types of signals (discrete/analogue), actuators. EDO2 - The student is able to select equipment for the required task, program microcontrollers and create circuits for the task. EDO3 - The student is able to work in a team, independently acquire and utilise new knowledge and skills in robotics. | |
| C# Programming | AIN1-051 | 5 | The concept of class. Basic elements of a class. Overloading operations in a class. Inheritance in C#. Object-oriented features of C# language. Working with files. Generalised classes (templates). Working with graphical objects. | Formation of students' theoretical knowledge and practical skills in the C# programming language. | EDO1 - The student knows the features of developing algorithms, libraries and software packages, system and application software products in the C# programming language. EDO2 - The student is able to develop architecture, algorithmic and software solutions of system and application software. EDO3 - The student is able to work in a team, independently investigate the development and use of tools, automated systems in scientific and practical activities. | |
| Basics of Economics | AIN1-052 | 5 | Introduction to economics. Subject matter and method. Basic economic concepts. Market system: supply and demand. Costs and profits of the firm. Financial and monetary system. Inflation and unemployment. Open economy | Formation of systemic economic thinking in students to understand the economic logic of the laws of development of society, processes and phenomena occurring at the micro level, mastery of modern methods of microeconomic analysis with the possibility of further application of acquired knowledge in practical activities. | EDO1 - The student knows the general conceptual apparatus of economic theory, the basics of the theory of public choice, economic efficiency and political decision-making. EDO2 - The student is able to identify problems of economic nature when analysing specific situations, to propose ways of solving them, to use knowledge of economics and economic policy to solve professional problems. EDO3 - The student is able to acquire new knowledge using modern educational and information technologies and to work independently with economic literature | |
| Module | Subject # | ECTS credits | Content | Objectives | Learning outcomes |
© INAI.KG 2024
With by
onoi.dev