This Research Course will provide students with the conceptual, theoretical, and implementational foundations of fundamental tools for structured learning and inference: probabilistic graphical models, probabilistic programming, and deep neural networks. The course will focus on the design and training of structured models for specific application use cases such as answering probabilistic queries over data, sequence tagging and classification, and image recognition through programming intensive projects including a final independently proposed research project with report component.