AMS490: Applied Math for AI (Spring 2027) covers the math behind modern AI: multivariable calculus, linear algebra, and optimization from gradient descent to Adam. Students apply it to how neural networks learn, including automatic differentiation, backpropagation, and softmax with cross-entropy, ending in a final project that builds a complete model and makes sense of the math inside it.