This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2025-2026 (archived)
Module COMP2181: Theory of Computation
Department: Computer Science
COMP2181: Theory of Computation
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 2 | 20 | Available in 2025/2026 | Durham |
Pre-requisites
- COMP1081 Algorithms and Data Structures
Corequisites
- None.
Excluded Combinations
- 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 provide assessment (both formative and summative) to guide students in the correct development of their knowledge and skills.
- The end of year examination assesses the knowledge acquired and the ability to use this knowledge to solve problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 42 | 2 per week | 1 hour | 42 | |
| Problem Classes | 20 | 1 per week | 2 hours | 40 | |
| Preparation and Reading | 118 | ||||
| Total | 200 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Coursework | 34% | General Test | 50% | ||
| General Test | 50% | ||||
| Examination | 66% | On Campus Written Examination | 2 hours | 100% |
Formative Assessment
Example exercises given through the course.
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.