Mathematics

This course features studying a mathematics book in a seminar style, providing students with basic training to learn from mathematical literature independently and make presentations of knowledge.
 

Students study the application of statistical and computational methods to decision-making problems in management. Linear programming models for resource allocation; sensitivity analysis and duality; multiple management objectives using goal programming; network flow models for transportation, job-scheduling and inventory management; integer linear programming; network-representations; resource-levelling and time-cost tradeoffs, stochastic optimization. 

This course introduces mathematical and programming skills that are employed by researchers in the Molecular and Biophysical Life Sciences to analyze and integrate data and to understand the physics of living systems. The course is divided into two parts that run in parallel. The mathematics part of the course consists of nine lectures that cover: basic algebra, goniometry, differentiation and integration (including functions of multiple variables), limits, (partial) differential equations (first order and second order), Taylor expansion, basic probability theory and statistics and vectors (including dot product and cross product). Each lecture is followed by a supervised practical session. The programming part consists of six lectures that introduce the basics of programming by discussing the modulare structure of programs (modules, functions, loops), different data types and variables, as well as good practices. For some calculations of the mathematics part of the course it is explained how to perform those calculations using Python. After each lecture, students work individually on a series of practical coding assignments that familiarize them with the basics of programming in Python during supervised tutorials, where regular instruction and feedback is provided.

The course focuses on key algebraic structures including ring and polynomial theory, with a strong emphasis on mathematical proofs and applications of algorithms including Euclid's, Lagrange interpolation, RSA cryptography, and the Fast Fourier Transform.

This course covers the following topics and subtopics: reduction of endomorphisms, determinants, eigenvectors, and eigenvalues; characteristic polynomials and minimal polynomials; Cayley-Hamilton Theorem; diagonalization and trigonalization; Dunford and Gauss-Jordan Reductions; Hermitian and Euclidean spaces; bilinear forms; quadratic forms; self-adjoint; and orthogonal groups in 2 or 3 dimensions.

This course features a study of selected topics in Linear Algebra in continuation of Linear Algebra II: eigenvalues, eigenvectors, diagonalization and Jordan normal form. 

This is a special studies course involving an internship with a corporate, public, governmental, or private organization, arranged with the Study Center Director or Liaison Officer. Specific internships vary each term and are described on a special study project form for each student. A substantial paper or series of reports is required. Units vary depending on the contact hours and method of assessment. The internship may be taken during one or more terms but the units cannot exceed a total of 12.0 for the year.