Graphs, Tensors, and AI Computing. 1 credit, 1 contact hour

Catalog description

Prerequisites: CS 100 and MATH 337 with a grade C or better. This course equips students with the computational tools and systems-level understanding that make the linear algebra of MATH 337 concrete and usable in the context of Artificial Intelligence and Computer Science. Students develop hands-on fluency with the linear algebra of MATH 337 , writing vectorized code and reasoning about the asymptotic complexity and hardware cost of matrix operations. A particular emphasis is placed on the graph-theoretic dimension of linear algebra, including adjacency matrices, graph Laplacians, and spectral structure, as a central bridge between matrix algebra and CS. The computing skills developed here are the foundation for machine learning, AI, and CS coursework: understanding how data is laid out in memory, how sparsity reshapes computational cost, and how GPU computation with PyTorch tensors changes the economics of matrix arithmetic. Throughout, students develop the ability to move fluidly between mathematical reasoning and computational implementation.