Course Catalog

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TOPICS IN MATHEMATICS (MA1910)

Topics vary by semester.

OPERATIONS RESEARCH (MA2007)

This course is intended to study the computational methodologies of Linear Programming and its variants and extensions, from the Transportation Problem and the Assignment Model to Network optimisation.
Various types of applications from the fields of Environmental Science (for the determination of the efficient use of scarce resources), Economics, Finance, Advertising... will be investigated and the methods by which useful results are obtainable - together with the reasoning behind the use of these methods - will be discussed.
Both the mathematical aspects and the use of a software package will be highlighted, each approach reinforcing the other. All classes will be held in the computer lab so as to enhance understanding, favour an interactive approach and develop new insights.
Prerequisite: MA 1010 or above

APPLIED STATISTICS II (MA2020)

Familiarizes students with several types of multivariate statistics methods with respect primarily to applications and interpretations in the area of social sciences. This course will cover the data-analysis concepts and procedures used in applied and experimental psychology, economics, business and in general in social sciences. Emphasis will be given to the qualitative interpretation and manipulation of mathematical and statistical concepts, showing the students their effectiveness through concrete applications. Students will use appropriate software packages for labs and projects.

CALCULUS II (MA2030)

The continuation of MA1030, Calculus I. This course is appropriate for economics, mathematics, business and computer science majors and minors. Topics include: infinite series and applications; differential equations of first and second order and applications, functions of several variables, partial derivatives with applications, especially Lagrange multipliers. Includes the use of Mathematica.

LINEAR ALGEBRA (MA2041)

Treats applications in economics and computer science, limited to Euclidean n-space. Topics include: the linear structure of space, vectors, norms and angles, transformations of space, systems of linear equations and their applications, the Gauss-Jordan method, matrices, determinants, eigenvalues and eigenvectors. Uses Mathematica for graphics and algorithms.

TOPICS IN MATHEMATICS (MA2091)

Courses on different topics in the discipline, enriching the present course offerings. These classes are taught by permanent or visiting faculty.

DISCRETE MATHEMATICS (MA2400)

This course is designed to highlight discrete mathematical structures. Discusses propositional logic, proofs and mathematical induction, matrices of relations and digraphs, set theory and number bases, combinatorial analysis, graph theory and Boolean algebra.The prerequisite for this course is MA1010 or above or CS 1040.

PROBABILITY (MA3005)

Examines probability in its various components and through its diverse applications. Topics include: combinatorial analysis, axioms of probability, discrete random variables and distributions & continuous random variables and probability density functions, joint distribution functions, law of large numbers. The statistical concepts of conditioning, independence and expectation will be highlighted, as well as the notion of moments. Selected applications will shed light on the use of probability in various fields.

CALCULUS III (MA3030)

Examines examples from the physical sciences to illustrate the introduced concepts. Topics include: trigonometric and hyperbolic functions; polar coordinates, parametric curves and conic sections; vectors, curves and surfaces in space; vector fields, line integrals, theorems of Green and Stokes.

RESEARCH AND WRITING IN CS & MATHEMATICS (MA3050)

One of the most critical factors in the success of an idea is effective communication.

This course addresses written communication in different contexts for both Computer Science and Mathematics.

Students will learn to choose the appropriate styles for different audiences (e.g. textbooks, articles/reviews, posters, general public, professional colleagues).
They will be asked to research and report on small practical tasks in bot CS and Math, such as proof, empiric complexity analysis of algorithmic strategies, data analysis and visualization and software documentation, producing well structured and formatted documents. Along the way, they will learn to use mark-up languages (such as LaTeX and markdown) and dedicated software for plotting and graphics editing.