Nasza Katedra organizuje w ramach konferencji ICCI 2026 sesję specjalną pt.  "Advanced Machine Learning: Quantum, Data-Driven, and Hybrid Approaches".

Poniżej przedstawiamy opis sesji w języku angielskim.

 This Special Session focuses on advanced and contemporary approaches to machine learning that bring forward new computational principles, expressive model structures, and techniques for improving modeling, training, and inference. Contributions are welcome in areas where quantum algorithms, hybrid optimization methods, or data-driven techniques provide measurable improvements in efficiency, interpretability, scalability, or decision-support capabilities. Relevant directions include quantum-enhanced learning algorithms, hybrid methods combining numerical optimization with parameterized quantum circuits, interpretable model architectures inspired by concepts such as the Kolmogorov-Arnold (KAN) representation, and techniques for processing, integrating, or fusing large, heterogeneous, and unstructured data.

Approaches supporting high-dimensional analysis, robust learning, or knowledge-intensive applications are also within scope.

Topics of interest include, but are not limited to:

• quantum and hybrid machine learning,
• scalable data-driven models for large or heterogeneous datasets,
• interpretable and structure-aware learning architectures,
• data fusion and integration techniques for machine learning,
• optimization and decision support enhanced by ML methods,
• applications of ML to complex, distributed, or multi-source data.