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.

ECTS breakdown: 3 ECTS lecture (24 hours)

Schedule: June 2027

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.

ECTS breakdown: 1 ECTS lecture (8 hours); 1 ECTS practical (15 hours)

Schedule: January–February 2027

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.

ECTS breakdown: 2 ECTS lecture (16 hours)

Schedule: 3–19 December 2026

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.

ECTS breakdown: 1 ECTS lecture (8 hours); 1 ECTS practical (15 hours)

Schedule: January–February 2027

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.

ECTS breakdown: 3 ECTS lecture (24 hours)

Schedule: February 2027

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.

ECTS breakdown: 1 ECTS lecture (8 hours)

Schedule: December 2026

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.

ECTS breakdown: 2 ECTS lecture (16 hours); 1 ECTS practical (15 hours)

Schedule: First 2–3 weeks of December 2026

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.

ECTS breakdown: 1 ECTS lecture (8 hours)

Schedule: January–February 2027

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.

ECTS breakdown: 2 ECTS lecture (16 hours); 1 ECTS practical (15 hours)

Schedule: TBA

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.

ECTS breakdown: 2 ECTS lecture (16 hours)

Schedule: January–February 2027

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.

ECTS breakdown: 1 ECTS lecture (8 hours); 1 ECTS practical (15 hours)

Schedule: 28 January; 2, 4, 9, 16, 18, 23 and 25 February 2027 (14:30–17:30, except 28 January: 14:30–16:30)

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.

ECTS breakdown: 1 ECTS lecture (8 hours); 1 ECTS practical (15 hours)

Schedule: TBA

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.

ECTS breakdown: 2 ECTS lecture (16 hours); 1 ECTS practical (15 hours)

Schedule: TBA