Mathematics

This course examines examples of statistical data and the use of graphical means to summarize the data. It covers basic distributions arising in the natural and behavioral sciences; the logical meaning of a test of significance and a confidence interval; and tests of significance and confidence intervals in the one and two sample setting (means, variances and proportions).

This course examines basic properties of metric spaces; openness; closedness; interior; closure; derived set; boundary; compactness; completeness; continuity; connectedness; pathwise connectedness; uniform continuity; uniform convergence; and Banach's fixed point theorem.

This course will focus on basic mathematical models and methods in financial calculation analysis. Specifically, it includes basic problems in financial mathematics: basic concepts and methods of interest calculation, calculation of annuity cash flow model, basic method of calculating general investment return rate, principal and interest decomposition process of cash flow, fixed income securities, interest risk analysis, financial income in random situations.

Mathematical logic is a fundation of mathematics and computer science, and foundation course for the students of mathematics and computer science. The content of this course includes propositional logic, first-order (predicate) logic and basic mathematical systems.

Probability is to describe the measure of the likelihood of random events. Probability theory is to make research through simple random events and gradually into complex random events. Probability theory is an efficient method and tool to study complex random phenomena. It is also the base to learn statistics.

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.