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AMATH 345: Data-Driven Mathematical Models (new from 2025)
Introduces data-driven mathematical methods for modelling and prediction of complex systems in Science, Medicine, and Technology. (Includes first introduction to neural network models.)
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AMATH 449 / CS 479: Neural Networks (co-taught with Computer Science)
Introduces neural network methods, including feed-forward networks, convolutional networks, backpropagation, stochastic gradient descent, recurrent networks, etc.
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AMATH 445: Scientific Machine Learning (since 2023)
Introduces deep learning architectures and algorithms and provides an in-depth exploration of how deep learning techniques are applied in various scientific and medical domains.
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AMATH 495: Introduction to the Mathematics of Deep Learning (new from 2025)
Theoretical introduction to the mathematics of deep learning: approximation theory, optimization theory, generalization theory.
(See also the complete AM-SciML calendar page)
(Note: These new AMATH courses can also be taken as part of the regular Applied Mathematics major.)
(Note: Further machine learning courses on the AM-SciML program include STAT441: Statistical Learning-Classification and STAT444: Statistical Learning-Advanced Regression.)
