Mathematics for Artificial Intelligence

Mathematics and numerical algorithms for artificial intelligence and machine learning. Linear algebra: vector spaces; linear maps; orthogonality; singular-value decomposition; linear systems; eigenspaces; determinant; Schur decomposition. Calculus: gradient, Hessian, Jacobian matrix; back-propagation; Optimization: unconstrained minimization; Lagrange multipliers; gradient descent; Newton's method. Applications: principal component analysis; linear regression; rigid transformations; logistic regression classifier. Prerequisite: COMPSCI 101 or 201, and MATH 111L or 112L. Fluency in Python recommended. Not recommended for students who have taken MATH 216, 218, or 221.