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Applied Artificial Intelligence

7M06108 Applied Artificial Intelligence

Specialized subjects: algorithms and data structures, as well as databases.

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.

Contacts

Admission Committee

(7172) 64-57-10
info@astanait.edu.kz

Mon-Fri 9:00 – 18:00

Objective of education program

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.  

List of a specialist’s positions

career opportunities
  • Data Analyst;
  • Data Scientist;
  • Big Data AI Analyst;
  • Computer Vision Engineer;
  • AI Developer;
  • NLP Engineer;
  • AI Consultant;
  • Big Data Engineer;
  • head of the organization;
  • deputy head of the structural unit;
  • head of the structural unit;
  • expert of the republican center;
  • employee of the national scientific and practical center, university.

M094 – Information technology

Group of educational programs

Master of Technical Science in Educational Program «7M06108 Applied Artificial Intelligence»

Awarded degree

2 years

Duration of education

Learning outcomes

  • Formulate the tasks that arise in the course of scientific research, apply methodological and psychological approaches in research.
  • Possess critical thinking and master the academic language at a professional level, which allows conducting scientific research and teaching special disciplines in universities
  • Apply artificial intelligence methods to solve various applied problems.
  • Formulate, modify, and develop artificial intelligence and machine learning methods.
  • To develop artificial intelligence systems aimed at solving a wide range of applied tasks in various fields of activity.
  • To be proficient in a foreign language at a professional level, which allows conducting scientific research and teaching special disciplines in universities.
  • Choose the necessary research approaches and methods, modify existing ones and develop new ones based on the objectives of a particular study.
  • Apply big data analysis techniques, including their preprocessing, visualization, and extraction of meaningful information.
  • Manage the team and lead the process of developing artificial intelligence systems.
  • Develop solutions based on neural networks, design and train neural networks for processing images, text and other data.

Documents

List of topics for the master's program

Development Plan

Graduate model

Documents of the educational program

Academic disciplines

Cycle of fundamental disciplines

University’s component

Psychology of Management

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.

Higher Education Pedagogy

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.

Foreign language (professional)

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 Practicum (Internship)

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.

History and Philosophy of Science

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.

AI Essentials: Theory and Applications

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

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.

Mathematical fundamentals of AI

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.

Cycle of major disciplines

University’s component

Introduction to Multi-Agent Systems

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.

Teaching methods and strategies

This course explores innovative pedagogical approaches and effective techniques for engaging and facilitating learning in diverse educational settings.

Cognitive technologies and decision support systems

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 practice

Research work of a master's student, including an internship and the completion of a master's thesis

Research work of a master’s student, including an internship and the completion of a master’s thesis

Cycle of major disciplines

Elective component

Advanced machine learning

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.

Artificial Intelligence in data analysis

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.

AI image processing

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.

Artificial intelligence and neural networks

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.

Big data methods and tools

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.

Computer graphics and modeling in AI

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.

Modern deep learning technologies

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.

Data-Driven AI: Big Data & RL Approaches

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.

Algorithms and Methods of Reinforcement Learning

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.

Computer vision and image recognition

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.

Big Data cases: Industry 4.0, medicine, IoT

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.

Generative algorithms

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.