This course provides essential tools and concepts for using R, a powerful software for statistical computing and graphics, alongside Geographic Information Systems (GIS). Learners will explore R's interface, data handling, and linear modeling, while also integrating GIS applications to enhance their data analysis skills.
This course explores the innovative field of computational cameras, focusing on how they capture visual information through advanced optics, sensors, and processing techniques. Learners will engage with various camera technologies and their applications in diverse fields such as art, science, and social media.
This course covers the fundamentals of deep learning, including both theory and applications. Topics include neural net architectures (MLPs, CNNs, RNNs, graph nets, transformers), geometry and invariances in deep learning, backpropagation and automatic differentiation, learning theory and generalization in high dimensions, and applications to computer vision, natural language processing, and robotics.
