Introduces the legal frameworks, ethical principles, and business models relevant to Artificial Intelligence. Students explore issues such as data protection, intellectual property, privacy, and sustainability. The course develops skills to design and manage AI projects responsibly in industrial and communication contexts. Real case studies of success and failure illustrate practical challenges. By the end, students can integrate legal, ethical, and entrepreneurial perspectives into AI solutions.

Provides students with the essential software and platforms to develop AI solutions. It covers programming in Python and MATLAB, data management, and the use of libraries and frameworks such as NumPy, Pandas, TensorFlow, and PyTorch. Students also learn to work with biomedical, acoustic, and remote sensing signals. Cloud computing tools like AWS and Google Cloud are introduced to ensure scalability and efficiency. By the end, students can implement and document AI solutions using professional tools and collaborative environments.

Introduces mathematical and computational methods to solve complex optimization problems in engineering. Students study linear programming, combinatorial optimization, and heuristic approaches such as genetic algorithms. The course emphasizes resource allocation, process efficiency, and decision-making in industrial and communication contexts. Practical exercises and case studies help translate theory into real-world problem solving. By the end, students will be able to model and apply optimization techniques to engineering challenges.

Introduces fundamental supervised and unsupervised learning methods. Students explore regression, classification, clustering, and semi-supervised approaches using real-world datasets. The course emphasizes performance evaluation, parameter tuning, and practical implementation of algorithms. Case studies illustrate how ML supports decision-making in industrial and communication fields. By the end, students will be able to design, train, and validate basic machine learning models.

Focuses on preparing data for effective use in AI systems. Students learn techniques for data extraction, cleaning, transformation, and dimensionality reduction. Validation methods such as cross-validation and performance metrics are applied to ensure data quality. Practical exercises demonstrate the impact of preprocessing on machine learning outcomes. By the end, students will be able to design robust preprocessing pipelines for diverse datasets.

Provides the theoretical and practical foundations of artificial neural networks. Students study key architectures such as convolutional neural networks (CNNs) and recurrent networks (RNNs, LSTM, GRU). The course covers training methods, optimization strategies, and regularization techniques. Real-world applications in image, signal, and language processing illustrate their impact. By the end, students can design, train, and evaluate deep learning models for complex tasks.

Introduces advanced AI paradigms beyond traditional supervised learning. Students learn the principles of agent–environment interaction, reward optimization, and policy learning. The course also explores generative models such as GANs for creating synthetic data and content. Case studies highlight industrial and communication applications of these techniques. By the end, students will understand and apply reinforcement and generative methods to real-world challenges.

Explores how AI enhances reliability and efficiency in industrial systems. Students learn to design models for monitoring, anomaly detection, and failure prediction. The course covers AI-driven quality control, predictive analytics, and maintenance strategies. Real examples show how these tools reduce costs and improve productivity. By the end, students can apply AI techniques to optimize quality management and maintenance processes.

Focuses on applying AI to improve efficiency in industrial operations. Students study methods for demand forecasting, inventory control, and scheduling. The course also covers optimization of transport routes, resource allocation, and fleet management. Case studies illustrate the impact of AI on supply chain performance and productivity. By the end, students will be able to design AI-based solutions for logistics and production challenges.

Teaches how AI can optimize renewable energy production and distribution. Students explore forecasting techniques, predictive analytics, and intelligent control systems for smart grids. The course emphasizes efficiency, sustainability, and integration of renewable resources. Practical cases demonstrate AI applications in planning and managing energy networks. By the end, students will be able to design AI-based strategies for energy optimization.

Explores how artificial intelligence transforms modern communication systems. Students learn techniques for optimizing transmission, managing network resources, and improving system performance. The course covers reinforcement learning and data-driven methods for advanced telecom applications. Case studies illustrate innovations in 5G, IoT, and connected technologies. By the end, students will be able to apply AI to design and optimize telecommunication systems.

Introduces AI techniques for analyzing and understanding human communication. Students learn methods for speech recognition, audio analysis, and natural language processing. The course covers both symbolic and statistical approaches, including speech-to-text and text-to-speech systems. Practical applications show how AI enables voice assistants, transcription, and language technologies. By the end, students will be able to design and implement basic audio and language processing solutions.

Focuses on applying AI to analyze data collected from satellites and remote sensors. Students study principles of Earth observation, data formats, and processing techniques. The course emphasizes deep learning models for image classification, object detection, and environmental monitoring. Case studies include climate analysis, natural hazard detection, and land-use mapping. By the end, students will be able to design AI solutions for environmental and geospatial challenges.

Introduces methods for analyzing physiological signals using AI. Students learn to process data such as ECG, EEG, and other biomedical signals to extract clinically relevant information. The course covers feature extraction, classification, and pattern recognition techniques. Practical examples demonstrate applications in diagnostics and healthcare monitoring. By the end, students will be able to design AI-based solutions for biomedical data analysis.

Explores AI applications in robotics and autonomous systems. Students study techniques for map-based navigation, simultaneous localization and mapping (SLAM), and semantic navigation without maps. The course integrates perception, decision-making, and control strategies for mobile robots. Case studies illustrate uses in assistive robotics, drones, and autonomous vehicles. By the end, students will be able to design AI-based solutions for navigation and localization challenges..

Examines how AI enables intelligent interaction among connected devices. Students learn methods for integrating data from multiple sensors to create autonomous and adaptive IoT systems. The course covers communication protocols, data fusion techniques, and real-time decision-making. Applications include smart cities, industrial automation, and healthcare monitoring. By the end, students will be able to design and program AI-driven IoT solutions.

Focuses on extracting and interpreting information from digital images. Students study fundamental techniques such as filtering, segmentation, and feature detection. The course also introduces AI-based models for object recognition and scene understanding. Applications include medical imaging, industrial inspection, and autonomous systems. By the end, students will be able to design and implement computer vision solutions using AI.

Gives students the opportunity to apply their AI knowledge in a real professional environment. Internships are carried out in companies, research centers, or institutions collaborating with the program. Students gain hands-on experience in solving practical problems and working on AI-driven projects. This exposure helps them build professional skills, industry connections, and career opportunities. By the end, students will have integrated academic learning with real-world practice.

Is the capstone of the Master’s program, where students integrate the knowledge and skills acquired throughout their studies. Working individually under academic supervision, they design and develop an AI-based project addressing a real problem in industry, communications, or research. The project includes both technical implementation and critical analysis. Results are presented and defended before an academic committee. By the end, students demonstrate their ability to apply AI in complex, professional contexts.