XLII Cycle, starting 2026–2027

The course introduces principles and methodologies for building robust, trustworthy, and sustainable AI systems. Topics include adversarial learning, Data-Centric AI, robustness to distribution shifts, data quality, and responsible deployment of machine learning systems in critical domains.

Schedule: TBD

The course explores graph-based methodologies for designing context-aware AI systems, including knowledge graphs, graph neural networks, graph embeddings, link prediction, and multimodal graph learning for relational and structured data.

Schedule: TBD

The course focuses on affective computing and emotion recognition using non-invasive biometric sensors. It covers sensor-based emotion analysis, feature extraction, supervised learning techniques, and applications in human-computer interaction and collaborative environments.

Schedule: TBD

The course presents advanced methods for learning from limited labeled data, including meta-learning, metric-based approaches, foundation models, self-supervised learning, and prompt-based adaptation techniques across vision, language, and multimodal domains.

Schedule: TBD

The course introduces heterogeneous computational architectures combining HPC, GPU acceleration, and hybrid classical–quantum workflows. Particular attention is devoted to intelligent interactive systems and adaptive AI-driven applications.

Schedule: TBD

The course presents interpolation techniques for structured and scattered data, including spline methods, subdivision schemes, edge-guided interpolation, Lanczos interpolation, and thin-plate splines, with applications in image, medical, and geospatial processing.

Schedule: TBD

The course investigates cybersecurity from a human-centered perspective, covering usable security, phishing, social engineering, authentication, mental models of cyber threats, and user-centered methodologies for secure system design.

Schedule: TBD

The course examines AI and machine learning approaches for analyzing neurodegenerative diseases through imaging, behavioral biometrics, and multimodal data, with applications to diagnosis, prediction, and healthcare decision support.

Schedule: TBD

The course introduces Symbiotic AI approaches combining natural language processing, knowledge representation, ontologies, knowledge graphs, reasoning systems, and large language models for interpretable and knowledge-driven AI.

Schedule: TBD

The course explores serious games as computational systems for healthcare, rehabilitation, and assessment. Topics include digital biomarkers, adaptive interventions, usability evaluation, and validation methodologies for interactive health technologies.

Schedule: TBD

The course introduces deterministic and swarm-intelligence optimization methods, including Particle Swarm Optimization and nature-inspired metaheuristics for search, path planning, and optimization problems in complex environments.

Schedule: TBD

The course explores machine learning paradigms for large-scale and streaming data, including ensemble learning, stream learning, transfer learning, model reuse, scalability, and data heterogeneity in Big Data environments.

Schedule: TBD

The course develops advanced scientific communication skills, including paper structure, academic writing, bibliography styles, reviewer response, research ethics, and the responsible use of AI tools in scientific publishing.

Schedule: TBD