Algebra of matrices, elementary theory of vector spaces and inner product spaces, the solution of simultaneous linear equations using Gaussian elimination and triangular factorization. Orthogonal projections, pseudo inverse and singular value decomposition, least squares approximation. Determinants, eigenvalues and eigenvectors, diagonalization.
Algebra of matrices, elementary theory of vector spaces and inner product spaces, the solution of simultaneous linear equations using Gaussian elimination and triangular factorization. Orthogonal projections, pseudo inverse and singular value decomposition, least squares approximation. Determinants, eigenvalues and eigenvectors, diagonalization.
Algebra of matrices, elementary theory of vector spaces and inner product spaces, the solution of simultaneous linear equations using Gaussian elimination and triangular factorization. Orthogonal projections, pseudo inverse and singular value decomposition, least squares approximation. Determinants, eigenvalues and eigenvectors, diagonalization.
Algebra of matrices, elementary theory of vector spaces and inner product spaces, the solution of simultaneous linear equations using Gaussian elimination and triangular factorization. Orthogonal projections, pseudo inverse and singular value decomposition, least squares approximation. Determinants, eigenvalues and eigenvectors, diagonalization.
Algebra of matrices, elementary theory of vector spaces and inner product spaces, the solution of simultaneous linear equations using Gaussian elimination and triangular factorization. Orthogonal projections, pseudo inverse and singular value decomposition, least squares approximation. Determinants, eigenvalues and eigenvectors, diagonalization.
The course is an introduction to strategic decision making and game theory. Ideas such as Nash equilibrium, dominant strategies, evolutionary stability, and asymmetric information are applied to a variety of strategic decision making problems taken from economics, computer science, politics, and biology.
A survey of the development of mathematics. Topics may include: the Egyptians and Babylonians, mathematics of the Greek Classical Age, Euclid and the Alexandrian School, the Renaissance, Fermat and the beginning of calculus, the work of Newton and Leibnitz, nineteenth century geometry, analysis and set theory.
This course introduces mathematical modeling in biology and other life science disciplines using discrete and continuous tools and techniques, including difference equations and differential equations. Students will learn to construct, analyze, and simulate models and interpret the results within their biological context.
Topics include: Euclid's algorithm, unique factorization moduli arithmetic, Fermat's and Euler's theorems, Chinese remainder theorem, RSA public key encryption, Pollard rho factoring, pseudo primes, error correcting codes, Hamming codes, polynomial rings and quotient rings, field extensions, finite fields and BCH codes.
A course in topology that may cover such topics as the classification of surfaces, knot theory, topological data analysis, and others. Contact the instructor for more information. May be taken prior to MA 351.
