The program trains specialists in the development and application of artificial intelligence technologies in real-world tasks. Students study machine learning, deep neural networks, data processing, computer vision, and natural language. The program teaches theoretical foundations and practical methods focused on real-world challenges and innovations in the rapidly evolving field of AI. The program offers the flexibility of choosing a destination and provides 3 selective destinations tailored to the interests of applicants.
Admission Committee
(7172) 64-57-10
info@astanait.edu.kz
Mon-Fri 9:00 – 18:00
Training of highly qualified specialists capable of developing and implementing modern artificial intelligence technologies, including machine learning, data processing and the creation of intelligent systems, to solve complex problems in various fields such as medicine, finance, industry and technology.
The content of the course is aimed at the formation of systemic ideas about the psychological laws of management, the specifics of using social and psychological knowledge and mastering the skills of analyzing the social and psychological principles that underlie effective management.
The content of the course is aimed at acquiring knowledge about the foundations of pedagogical theory and pedagogical skills, the management of the educational process for teaching in higher education, the formation of an understanding of the main categories of pedagogy, the place, role and significance of higher education pedagogy, understanding the basic principles of modern pedagogy and methodological approaches to solving pedagogical problems of higher education.
The aim of the course is to develop professional competences of specialists; to form professionally oriented communicative competence of master’s students, which allows them to integrate into international professional environment and use professional English as a means of intercultural and professional communication.
Teaching practice is a kind of practical activities of graduate students, including teaching, organization of educational activity of students, scientific and methodical work on the subject, obtaining skills in teacher’s work.
The content of the course is aimed at obtaining knowledge about the properties of science as a type of cognition and a socio-cultural phenomenon in its historical development by a master student; formation of system ideas about the general laws of scientific knowledge in its historical development and changing socio-cultural context.
This discipline involves studying the basics of machine learning and artificial intelligence, and applying this knowledge to solve real-world applied problems. The discipline covers many topics of learning with and without a teacher. The third type of machine learning tasks, the so-called attachment learning, is partially covered.
Programming for AI introduces Master’s students to essential programming concepts and tools for artificial intelligence development. The course covers Python, machine learning libraries, algorithm implementation, data processing, and optimization techniques, equipping students with practical skills for AI model development and deployment.
The course covers the mathematical foundations for developing solutions in the field of artificial intelligence. Master’s students study linear algebra, probability theory, statistics, optimization, and other key mathematical concepts used in machine learning and deep learning algorithms.
This course introduces Multi-agent Systems (MAS), covering the design, development, and interaction of autonomous agents. Master’s students learn the key concepts of distributed problem solving, coordination, collaboration, negotiation, and conflict resolution by applying these principles to real-world scenarios and technologies.
This course explores innovative pedagogical approaches and effective techniques for engaging and facilitating learning in diverse educational settings.
The course covers the basics of cognitive technologies and their application in decision support systems. Master’s students study methods of data analysis, behavior modeling, artificial intelligence, and machine learning for effective decision-making in complex situations.
Research practice
Research work of a master’s student, including an internship and the completion of a master’s thesis
This discipline involves a deeper study of machine learning and artificial intelligence, and the application of this knowledge to solve real-world applied problems. The discipline covers many learning topics such as supervised and unsupervised learning.
The course focuses on the use of artificial intelligence methods for data analysis. Learners will study machine learning, big data processing, clustering, and classification. They will explore algorithms for extracting knowledge from data, mastering tools for identifying hidden patterns, forecasting, and automating analytics, as well as applying them to real-world tasks for informed decision-making.
The course covers image processing techniques using artificial intelligence, including computer vision, neural networks, and deep learning. Master’s students will learn algorithms for analyzing, recognizing, and improving images, applying them to real-world tasks and projects.
The course examines the fundamentals of artificial intelligence and neural networks, including learning methods, network architectures, data processing and their application in various fields. Master’s students the design and implementation of AI-based and deep learning solutions.
The goal is to provide undergraduates with a deep understanding of big data concepts, technologies, and methodologies that enable them to process, analyze, and retrieve information from large-scale datasets in various domains.
The course is dedicated to computer graphics and modeling using AI. Master’s students will study the creation of 3D models, visualization, as well as the use of AI algorithms to improve graphical processes and create realistic virtual objects and scenes.
The course covers the theory and practice of deep learning, including neural networks, learning with and without a teacher, as well as applications in various fields: computer vision, natural language processing, and data analysis to solve real-world problems.
This course explores data-driven AI techniques, focusing on Big Data processing and Reinforcement Learning (RL) methods. Masters students will learn how to handle massive datasets, apply RL algorithms, and develop intelligent systems that adapt and improve. Practical applications in real-world AI scenarios will be emphasized through hands-on projects.
This course covers fundamental algorithms and techniques in reinforcement learning, including value-based, policy-based, and model-free methods. Students will explore applications in robotics, game AI, and optimization, gaining hands-on experience with modern frameworks to develop intelligent decision-making systems.
The course covers computer vision and pattern recognition techniques, including image processing, object detection, segmentation, and classification. Students study algorithms and technologies to develop systems capable of interpreting visual information and solving real-world problems.
The course is dedicated to analyzing big data cases in the context of Industry 4.0, medicine, and the Internet of Things (IoT). Master’s students study data processing and interpretation to improve production processes, medical solutions, and smart technologies.
This course introduces students to various generative modeling techniques and algorithms, such as variational autoshares (VAE), generative adversarial networks (GANS), and autoregressive models. Master’s students will learn how to develop, train, and evaluate generative models, as well as explore their applications in areas such as computer vision, natural language processing, and data synthesis.