Explore graph signal processing (GSP) techniques for learning graph structures from nodal observations in this webinar presented by Antonio G. Marques from King Juan Carlos University. Delve into the DEGAS (Data sciEnce on GrAphS) Webinar Series, organized in collaboration with the IEEE Signal Processing Society Data Science Initiative. Discover how to leverage GSP methodologies to infer relationships and connections between data points, enabling more effective analysis and interpretation of complex networked systems. Learn about cutting-edge approaches for graph learning, their applications, and potential challenges in various domains such as social networks, biological systems, and sensor networks. Gain insights into the intersection of graph theory, signal processing, and machine learning, and understand how these techniques can be applied to real-world problems involving interconnected data structures.