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This year-long course provides opportunities for undergraduate students in the Civil and Environmental Engineering and Geosciences departments to perform `full circle? science in the area of Arctic hydrology and geomorphology.

There are multiple growing concerns regarding issues of climate, class, race, gender identity, and the nature of democracy in our contemporary world. Science fiction has proven to be a thought-provoking genre to help raise awareness to many of these social and environmental issues. This course takes a global perspective on such pressing issues by examining science fiction films from around the world. As such, the course uses science fiction films as primary texts, accompanied by weekly readings.

Practical applications of technical design processes and methods of implementation using a project management approach. Specific opportunities derive from departmental projects and are extremely hands-on.

This course is an introduction to the history of Indigenous Peoples within the present-day borders of the U.S.A. and Canada. While we will only be able to cover a few culture groups in any depth, the major themes of the course relate to all groups: colonization, trade, land loss, sovereignty, religion and missionaries, treaties, war and peace, and identity. Another theme that runs throughout the course is the tension between history as understood and experienced by indigenous peoples and history as recorded and written by Euroamericans. Throughout, we will consider how ?history?

This course covers various aspect of neural networks, from fundamentals to advanced concepts. Topics include feed-forward neural networks, kernel-based approaches, deep learning, recurrent neural networks, Hopfield networks, Kohonen Self-Organized Maps, Grossberg Adaptive Resonance Theory, Helmholtz machines, MDL, Symbolic neural nets, and space-time neurodynamics, with links to computational neuroscience. Theoretical foundations of supervised, unsupervised, and reinforcement learning are described.

This course covers various aspect of neural networks, from fundamentals to advanced concepts. Topics include feed-forward neural networks, kernel-based approaches, deep learning, recurrent neural networks, Hopfield networks, Kohonen Self-Organized Maps, Grossberg Adaptive Resonance Theory, Helmholtz machines, MDL, Symbolic neural nets, and space-time neurodynamics, with links to computational neuroscience. Theoretical foundations of supervised, unsupervised, and reinforcement learning are described. This course counts as a CS Elective toward the CS major (BA or BS).

This course covers various aspect of neural networks, from fundamentals to advanced concepts. Topics include feed-forward neural networks, kernel-based approaches, deep learning, recurrent neural networks, Hopfield networks, Kohonen Self-Organized Maps, Grossberg Adaptive Resonance Theory, Helmholtz machines, MDL, Symbolic neural nets, and space-time neurodynamics, with links to computational neuroscience. Theoretical foundations of supervised, unsupervised, and reinforcement learning are described. This course counts as a CS Elective toward the CS major (BA or BS).