Applied Artificial Intelligence

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.