Overview
MATH5705 is an honours and postgraduate coursework mathematics/statistics course.
Units of credit: 6
Exclusions: MATH3570, MATH3611, MATH3610, MATH3620, MATH5645 (MATH3611 jointly taught)
Cycle of offering: Term 2
Graduate attributes: The course will enhance your research, inquiry and analytical thinking abilities.
More information: The Course outline will be made available closer to the start of term - please visit this website: www.unsw.edu.au/course-outlines
Important additional information as of 2023
UNSW Plagiarism Policy
The University requires all students to be aware of its policy on plagiarism.
For courses convened by the School of Mathematics and Statistics no assistance using generative AI software is allowed unless specifically referred to in the individual assessment tasks.
If its use is detected in the no assistance case, it will be regarded as serious academic misconduct and subject to the standard penalties, which may include 00FL, suspension and exclusion.
The online handbook entry contains information about the course. The timetable is only up-to-date if the course is being offered this year.
If you are currently enrolled in MATH5705, you can log into UNSW Moodle for this course.
Course overview
Limits and continuity are the central concepts of calculus in one and several variables. These concepts can be extended to quite general situations; the simplest of these is when there is some way of measuring the distance between two objects. Some of the most important examples of these `metric spaces' occur as sets of functions, so this course looks at ways in which one might say that a sequence of functions converges. Taking these ideas one step further, we look at convergence which does not come from a generalized distance function. These are the ideas of point set topology. The course will include topics such as countability, continuity, uniform convergence and compactness, as well as an introduction to the core areas of function analysis. This will include the notions of Banach and Hilbert spaces, including Reproducing Kernel Hilbert Spaces which are important in Applied Mathematics, Statistics and elsewhere.