Unit name | Topics in Discrete Mathematics 3 |
---|---|
Unit code | MATH30002 |
Credit points | 10 |
Level of study | H/6 |
Teaching block(s) |
Teaching Block 2C (weeks 13 - 18) |
Unit director | Dr. Walling |
Open unit status | Not open |
Pre-requisites |
MATH20002 Combinatorics and MATH21800 Algebra 2 OR MATH21100 Linear Algebra 2. For joint Mathematics and Computer Science students, it would be desirable to have taken COMS21103 Data Structures and Algorithms. |
Co-requisites |
None |
School/department | School of Mathematics |
Faculty | Faculty of Science |
Unit Aims
Ths is a topics course aimed at deepening and broadening the students' knowledge of various aspects of discrete mathematics, as well as illustrating connections between discrete mathematics and other areas such as algebra, probability, number theory, analysis and computer science.
Unit Description
Discrete mathematics refers to the study of mathematical structures that are discrete in nature rather than continuous, for example graphs, lattices, partially ordered sets, designs and codes. It is a classical subject that has become very important in real-world applications, and consequently it is a very active research topic.
This topics course exposes the students to a selection of advanced cutting-edge topics in discrete mathematics.
While results and problems of recent origin may be included in the syllabus, the instructors aim to make the material accessible to all students fulfilling the prerequisites by providing complete lectures notes and including all necessary background material.
Relation to Other Units
The course follows on from Combinatorics and complements Complex Networks and the Data Structures and Algorithms unit in Computer Science. The unit is likely to be of interest to those with an interest in number theory, algebra, probability and/or theoretical computer science.
Learning Objectives
In accordance with the specific syllabus taught in any particular year, students who successfully complete the unit should:
Transferable Skills
The ability to think clearly about discrete structures and the ability to analyse complex real-world problems using combinatorial abstractions.
Lectures, including examples and revision classes, supported by lecture notes with problem sets and model solutions. Self-study with directed reading based on recommended material.
100% Examination.
Raw scores on the examinations will be determined according to the marking scheme written on the examination paper. The marking scheme, indicating the maximum score per question, is a guide to the relative weighting of the questions. Raw scores are moderated as described in the Undergraduate Handbook.
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