Textbook in PDF format The contributions in this book offer new insights into the theoretical and practical challenges of supervised and unsupervised learning, highlighting the remarkable breadth of contemporary statistical research while maintaining methodological rigor. Innovative approaches to statistical modeling, addressing spatial dependencies and circular data structures, are presented alongside significant advances in interpretable machine learning that reconcile statistical precision with algorithmic transparency. Particularly noteworthy is the volume’s treatment of complex data structures, including novel methods for network analysis, high-dimensional clustering, temporal pattern recognition and optimization techniques. The volume interweaves methodological innovation and practical relevance, and the applications span diverse domains, including the social sciences and biomedical engineering, each demonstrating the effective translation of statistical theory into real-world impact