Explore graph constructions for machine learning applications in this IEEE Signal Processing Society webinar presented by Antonio Ortega from USC. Delve into basic definitions, graph signal variation, and semisupervised learning techniques. Examine graph signal sampling, active semisupervised learning, and theoretical analysis of conventional approaches. Investigate orthogonalization, linear embeddings, label propagation, and deep neural networks. Learn about supervised classification, smoothness regularization, local political interpolation, and motor model selection. Gain insights into the geometry of deep learning and discover new algorithms for enhancing machine learning applications using graph-based methods.