This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Postgraduate Handbook 2022-2023 (archived)
Module COMP52215: Advanced Algorithms and Discrete Systems
Department: Computer Science
COMP52215: Advanced Algorithms and Discrete Systems
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Tied | 5 | 15 | Available in 2022/2023 | None. | Durham |
Tied To
- G5T109: Scientific Computing and Data Analysis (Earth and Environmental Sciences)
- G5T209: Scientific Computing and Data Analysis (Financial Technology)
- G5T309: Scientific Computing and Data Analysis (Astrophysics)
- G5T409: Scientific Computing and Data Analysis (Particle Physics)
Pre-requisites
- PHYS52015 Introduction to Scientific and High Performance Computing
Corequisites
- n/a
Excluded Combinations
- n/a
Aims
- Provide advanced knowledge and critical understanding of paradigms, fundamental ideas, algorithms and methods behind the modelling and simulation of discrete systems
- Provide advanced knowledge and critical understanding of paradigms, fundamental ideas and methods behind advanced algorithms
Content
- Advanced Algorithms
- Discrete Systems
Learning Outcomes
Subject-specific Knowledge
- Understanding and critical reflection of advanced ideas, numerical techniques and algorithms used to study discrete models
- Understanding and critical reflection of advanced engineering algorithms in high-performance computing and data analysis
Subject-specific Skills
- Basic familiarity with state-of-the-art algorithms to solve large-scale and data intense challenges
- Competence to translate discrete problem descriptions into algorithmic formulations; competent and educated selection of appropriate solution algorithms
Key Skills
- Familiarity with advanced paradigms and modern algorithms underlying scientific computing for discrete systems, and their analysis
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Teaching will be by lectures and workshops.
- The lectures provide the means to give a concise, focused presentation of the subject matter of the module.
- When appropriate, the lectures will also be supported by the distribution of written material, or by information and relevant links on Ultra.
- Regular problem exercises and workshops will give students the chance to develop their theoretical understanding and problem solving skills.
- Students will be able to obtain further help in their studies by approaching their lecturers, either after lectures or at other mutually convenient times.
- Student performance will be summatively assessed through coursework.
- The formative coursework provides opportunities for feedback, for students to gauge their progress and for staff to monitor progress throughout the duration of the module.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures for Advanced Algorithms | 12 | 3 per week | 60 minutes | 12 | |
| Practical Classes for Advanced Algorithms | 4 | 1 per week | 60 minutes | 4 | |
| Lectures for Discrete Systems | 12 | 3 per week | 60 minutes | 12 | |
| Practical Classes for Discrete Systems | 4 | 1 per week | 60 minutes | 4 | |
| Self Study | 118 | ||||
| Total | 150 |
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Summative Coursework | 100% | Advanced Algorithms Coursework | 1 week | 50% | Yes |
| Discrete Systems Coursework | 1 week | 50% | Yes |
Formative Assessment
n/a
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.