Undergraduate Programme and Module Handbook 2018-2019 (archived)
Module COMP2181: THEORY OF COMPUTATION
Department: Computer Science
COMP2181: THEORY OF COMPUTATION
| Type | Open | Level | 2 | Credits | 20 | Availability | Available in 2018/19 | Module Cap | Location | Durham |
|---|
Prerequisites
- COMP1081 Algorithms and Data Structures OR (COMP1021 Mathematics for Computer Science OR MATH1031 Discrete Mathematics if taken in academic year 2017/18)
Corequisites
- None.
Excluded Combination of Modules
- None.
Aims
- To introduce students to: important models of computation and how they are related.
- fundamental notions of computation such as 'computable' and 'efficiently computable'.
- and the design and analysis of efficient algorithms.
Content
- Models of computation
- Basic computability theory
- Algorithm design
- Computational complexity
Learning Outcomes
Subject-specific Knowledge:
- To have an understanding of different models of computation and their relevance to computer science.
- To have an understanding of how algorithms can be used to solve fundamental problems within Computer Science.
Subject-specific Skills:
- On completion of the module, students will be able to demonstrate:
- an ability to use different models of computation in context of computer science
- an ability to apply and analyze algorithms for fundamental problems within computer science
Key Skills:
- On completion of the module, students will be able to:
- extract an abstract computational model from a real world problem
- distinguish between computationally tractable an intractable problems in computer science
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lecturing demonstrates what is required to be learned and the application of the theory to practical examples.
- Problem classes through practicals provide assessment (both formative and summative) to guide students in the correct development of their knowledge and skills.
- The end of year examinations assess the knowledge acquired and the ability to use this knowledge to solve problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total/Hours | |
|---|---|---|---|---|---|
| Lectures | 44 | 2 per week | 1 hour | 44 | |
| Practicals | 21 | 1 per week | 2 hours | 42 | |
| Preparation and Reading | 114 | ||||
| Total | 200 |
Summative Assessment
| Component: Coursework | Component Weighting: 34% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Practical work | 100% | Yes | |
| Component: Examination | Component Weighting: 66% | ||
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Examination | 2 hours | 100% | Yes |
Formative Assessment:
Example exercises given through the course.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University