Current course offerings

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Course description

This course will provide students with the mathematical foundations important for advanced statistics methods, data modelling and analysing large data sets. Ordinary differential equations, multi-dimensional integration and change of variables. Orthogonal matrices, eigenvalues, eigenvectors, diagonalization, method of least squares, singular value decomposition. Multivariate Gaussian & related techniques including principal component analysis. Taylor series, maxima, minima and saddle points in N-dimensions. Lagrange multipliers for constrained optimization. Examples from statistical methods will be used to illustrate application of the methods. Computational software will be used throughout this course.

Archived offerings