AI+X · Professional Master (1 year)

01 — About

Purpose and concept

The objective of the educational program is to prepare highly qualified practice-oriented and engineering specialists in the development, deployment, and application of artificial intelligence technologies in industrial and digital environments, as well as to develop competencies in designing, integrating, and managing AI solutions for intelligent digital and industrial systems.

The educational program “7M06102 AI+X” is aimed at preparing practice-oriented and engineering specialists in the application of artificial intelligence technologies in industrial and digital environments. The program develops competencies in machine learning, industrial analytics, data engineering, AI systems architecture, MLOps, IoT, and intelligent infrastructure.

The learning process is focused on practical case studies, development and deployment of AI systems, and the application of intelligent solutions in the context of digital transformation.

Graduates of the program may pursue professional careers in industrial enterprises, IT companies, AI and Data Science organizations, digital platforms, and high-tech industries.

Code and name	7M06102 AI+X (professional)
Field of education	7M06 — Information and communication technologies
Duration	1 year · 3 trimesters of 10 weeks
Volume	60 academic credits
Level	Level 7 (ISCED / NQF / SQF)
Degree	Master of Engineering and Technology
Final assessment	Master’s project defence

Admission requirements

02 — Tracks

Choose your specialisation

Track 1

Industrial AI and Smart Systems

AI for industry and intelligent infrastructure: predictive diagnostics, digital twins, IoT and Edge AI, optimisation of industrial and energy processes.

Industrial AI and predictive diagnostics
Digital twins
IoT and Edge AI in industry

Track 2

AI Systems Engineering and Security

Engineering of AI systems: architecture, MLOps, data engineering, distributed and cloud systems, integration and deployment, security and risk analysis.

AI systems architecture and MLOps
Advanced machine learning
AI systems integration and deployment

Track 3

AI for Unmanned Aerial Systems

AI for unmanned aerial systems: design and programming of intelligent UAVs, computer vision, swarm intelligence and cybersecurity.

Computer vision for UAVs
Swarm intelligence and multi-agent systems
UAS cybersecurity

03 — Curriculum

What the programme consists of

Select a course to read its description. Credits are academic credits; one credit equals 30 academic hours.

Core courses16 credits

Electives (tracks)19 credits

Industrial AIAI for Industrial Process Optimization and ControlThe course aims to study AI methods for industrial process control and optimization. It covers SCADA systems, Industrial IoT, reinforcement learning, predictive analytics, digital modeling, TensorFlow, PyTorch, automation approaches, monitoring, and techniques for improving operational efficiency.

AI SystemsAI Systems Architecture and MLOpsThe course aims to study AI system architecture design principles and MLOps approaches in industrial environments. It covers Docker, Kubernetes, MLflow, FastAPI, ML pipelines, microservice architecture, model monitoring, automation of AI solution deployment, and maintenance of AI systems on AWS, Azure, and Google Cloud platforms.

UASDesign, Control and Programming of Intelligent Unmanned Aerial SystemsThe course focuses on modern technologies for the design, control, programming, and operation of intelligent Unmanned Aerial Systems (UAS). It covers UAV architecture, flight control systems, autopilot programming, navigation and mission planning, sensor and payload integration, diagnostics and technical support, computer vision, machine learning, autonomous navigation, ROS2, PX4/ArduPilot platforms, and Artificial Intelligence methods for intelligent decision-making and autonomous control of unmanned aerial systems.

Industrial AIIndustrial AI and Predictive DiagnosticsThe course aims to study AI methods for predictive diagnostics of industrial equipment. It covers predictive maintenance, anomaly detection, sensor analytics, time-series forecasting, SCADA/IoT data processing, TensorFlow, PyTorch, and methods for improving industrial system reliability.

AI SystemsAdvanced Machine Learning for AI SystemsThe course aims to provide advanced knowledge of modern machine learning methods for complex AI systems. It covers deep learning, transformers, ensemble learning, reinforcement learning, TensorFlow, PyTorch, optimization algorithms, model evaluation, and methods for improving intelligent model performance.

UASComputer Vision for Unmanned Aerial SystemsThe course focuses on modern computer vision and digital image processing techniques for Unmanned Aerial Systems. Topics include object detection and recognition, image segmentation, video analytics, photogrammetry, remote sensing data processing, and deep learning methods. Special emphasis is placed on the integration of computer vision algorithms into autonomous navigation and intelligent UAV control systems.

Industrial AIDigital Twins and Intelligent Infrastructure SystemsThe course aims to study digital twin technologies for intelligent infrastructure systems. It covers simulation modeling, IoT integration, intelligent monitoring, BIM/GIS platforms, stream data processing, predictive analytics, and modeling of complex infrastructure and energy systems.

AI SystemsAdvanced MLOps and AI Lifecycle ManagementThe course aims to study advanced MLOps approaches and AI model lifecycle management. It covers ML pipelines, the use of Kubeflow and MLflow, CI/CD processes for machine learning models, model versioning, monitoring and maintenance of AI systems, drift detection, automated retraining, and ensuring reproducibility of results.

UASSwarm Intelligence and Multi-Agent Systems for UAVsThe course explores the theoretical and practical foundations of collaborative operations in Unmanned Aerial Systems. It covers swarm intelligence algorithms, multi-agent systems, cooperative navigation, task allocation, inter-agent communication, distributed decision-making, and Artificial Intelligence methods for coordinating autonomous UAV teams.

Industrial AIIoT and Edge AI in IndustryThe course aims to study the application of IoT and Edge AI technologies in industrial environments. It covers edge computing, distributed sensors, MQTT protocol, embedded AI, computer vision on edge devices, real-time analytics, industrial IoT architectures, and integration of intelligent devices into industrial processes.

AI SystemsAI Systems Integration and DeploymentThe course aims to develop skills in integrating and deploying AI systems in corporate and industrial environments. It covers enterprise architectures, API interaction, microservice integration, deployment of AI solutions on cloud platforms, testing, monitoring, and operation of intelligent systems in production environments.

UASCybersecurity of Unmanned Aerial SystemsThe course focuses on contemporary approaches to information and cybersecurity for Unmanned Aerial Systems. It covers secure communication protocols, cryptographic data protection, security of telemetry and command-and-control links, resilience of navigation systems, cyber threat detection and prevention, and the application of Artificial Intelligence techniques to enhance the cyber resilience of autonomous aerial platforms.

Practice, research work, final assessment25 credits

04 — Learning outcomes

What you will be able to do

LO1Develop AI solutions to improve the efficiency of organizations and industrial systems in digital and industrial transformation contexts, apply system analysis methods, and make data-driven management decisions.
LO2Apply data collection, storage, processing, and analysis methods, and use analytical and visualization tools to solve complex technical and applied problems.
LO3Apply machine learning and artificial intelligence methods, design, train, test, and optimize AI models, evaluate their performance, and use them for industrial problem solving.
LO4Design AI system architectures, apply data engineering methods, develop scalable high-load intelligent systems, and integrate them into corporate infrastructures.
LO5Deploy AI models in production environments, apply MLOps tools, manage model lifecycles, perform monitoring, and ensure the reliability of intelligent systems.
LO6Apply AI technologies to optimize industrial processes, energy systems, IoT infrastructures, and intelligent production environments, and develop efficient digital solutions.
LO7Design distributed and cloud-based AI systems, manage data infrastructures, and implement intelligent solutions in high-load computing environments.
LO8Develop digital products based on generative AI, define their functional capabilities, create prototypes, and deploy AI solutions in practical environments.
LO9Ensure data security, assess risks, comply with cybersecurity requirements, and maintain the stability of intelligent systems during AI solution development and deployment.
LO10Conduct applied research in business analytics and artificial intelligence, analyze and interpret results, prepare analytical reports and technical documentation, communicate professionally, and present results in professional and international environments.

05 — Careers

Where graduates work

The professional activity area of graduates includes organizations involved in the development and deployment of artificial intelligence technologies, data processing and analytics, intelligent transformation of digital and industrial systems, and implementation of AI solutions in corporate and industrial environments.

Graduates may pursue professional careers in IT companies, industrial and energy enterprises, telecommunications organizations, digital platforms, Smart Infrastructure and Industry 4.0 organizations, AI and Data Science centers, as well as high-tech and engineering companies.

Professions from the Atlas of New Professions: AI Technologist; Business/Working Process Transformation Specialist.

Data AnalystData EngineerMachine Learning EngineerArtificial Intelligence EngineerAI Systems ArchitectMLOps EngineerComputer Vision SpecialistData Processing and Analytics SpecialistAI Systems Deployment and Support SpecialistIndustrial and Digital Process Optimization SpecialistAI Project ManagerExpert in high-tech companiesHead of Department and Deputy Head of Department

06 — Key dates

Upcoming intakes

How to apply

Intake 1

Applications25 October — 7 November
Enrolment9 November — 13 November
Classes start16 November

Intake 2

Applications20 November — 5 December
Enrolment7 December — 11 December
Classes start14 December

Intake 3

Applications20 January — 5 February
Enrolment8 February — 12 February
Classes start15 February

Ready to step into an AI career?

Leave a request and the admissions office will help you choose a programme and an intake.