Programme Outlines and Overviews

The course contains elements from various fields of mathematics and mathematical statistics used when intelligent systems and machine learning are developed, used and analyzed. The course includes the following elements: - Vector and matrix calculations, linear maps Rn to Rm, eigenvectors and eigenvalues. - Partial and total order relations, complexity, Big-O notation - Partial derivatives, gradients, local convexity and extrema for smooth functions Rn to R - Basic probability theory, Bayes’ theorem - Discrete and continuous random variables - Probability distributions, in particular binomial and normal distribution - Point and interval estimation - Hypothesis tests - Single factor design experiments