This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2012-2013 (archived)
Module COMP4071: THEORETICAL COMPUTER SCIENCE IV
Department: Computer Science
COMP4071: THEORETICAL COMPUTER SCIENCE IV
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 4 | 20 | Not available in 2012/2013 | None. | Durham |
Pre-requisites
- Theoretical Computer Science III
Corequisites
- None
Excluded Combinations
- None.
Aims
- To introduce students to applications of theory of computation in economics.
- To give students a detailed knowledge of algorithmic solutions for computationally hard problems.
- To extend the students' knowledge of the latest advances in understanding hardness of computation.
Content
- Topics to be selected from:
- Algorithmic game theory.
- Probabilistic algorithms.
- Approximation algorithms.
- Fixed-parameter algorithmics.
- Interactive proofs.
- Hardness of approximation and the PCP theorem.
- Cryptography and zero knowledge proofs.
- Relativised computation and natural proofs.
- Circuit complexity.
- Theory and practice: graph-theoretical methods for practical problems.
Learning Outcomes
Subject-specific Knowledge
- On completion of this module, students will be able to demonstrate:
- an understanding of how the theory of computation is applied
- an understanding of a variety of approaches to the algorithmic solution for hard problems
- an awareness of the latest advances in research on hardness of computation.
Subject-specific Skills
- On completion of this module, students will be able to demonstrate:
- an ability to apply notions from the theory of computation
- an ability to choose the best way to tackle hard computational problems
- an ability to demonstrate hardness of computational problems
- an ability to appreciate research on the cutting edge of the theory of computation.
Key Skills
- On completion of this module, students will be able to demonstrate: a capacity for independent self-learning and research.
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.
- Homework problems identify areas where futher independent research should be conducted.
- Summative assessments test the knowledge acquired and the students' ability to use this knowledge to solve complex problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| lectures | 44 | 2 per week | 1 hour | 44 | |
| problem classes | 11 | 1 per 2 weeks | 1 hour | 11 | |
| preparation and reading | 145 | ||||
| Total | 200 |
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Examination | 66% | Examination | 2 hours | 100% | No |
| Coursework | 34% | Practical work | 100% | No |
Formative Assessment
Example formative exercises given during the course. Additional revison lectures may be arranged in the modules lecture slots in the 3rd term.
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.