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 COMP4197: Randomised Algorithms and Probabilistic Methods
Department: Computer Science
COMP4197: Randomised Algorithms and Probabilistic Methods
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 4 | 10 | Available in 2025/2026 | None. | Durham |
Pre-requisites
- COMP2181 Theory of Computation
Corequisites
- None
Excluded Combinations
- None
Aims
- The aim of the module is to equip students with the ability to design and analyse efficient probabilistic algorithms.
Content
- To be chosen from the following:
- basic bounds and inequalities (Markov, Chebyshev, Chernoff)
- Martingales
- Markov chains and random walks
- the probabilistic method
- approximate counting
- parallel and distributed probabilistic algorithms
Learning Outcomes
Subject-specific Knowledge
- On completion of the module, students will be able to demonstrate:
- a knowledge about various important problem solving paradigms in the broad area of probabilistic methods and algorithms
- an ability to apply techniques and methods from the relevant topics to tackle fundamental algorithmic problems
- an ability to conduct review and self-study to further their knowledge beyond the taught material
Subject-specific Skills
- On completion of the module, students will be able to demonstrate:
- an ability to apply methods and techniques from various areas of algorithmic design and probability theory
- an ability to reason with and apply methods of mathematical proof
Key Skills
- On completion of the module, students will be able to demonstrate:
- an ability to think critically
- an ability to work with abstract problems
- an ability to undertake general problem solving
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures enable the students to learn new material relevant to the design of probabilistic algorithms, as well as their applications.
- Formative assessments assess the application of methods and techniques, and examinations in addition assess an understanding of core concepts.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 22 | 2 per week | 1 hour | 22 | |
| Preparation and Reading | 78 | ||||
| Total | 100 | ||||
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Examination | 100% | Online Examination | 2 hours within 3 hour timed window | 100% |
Formative Assessment
Example formative exercises are given during the course. Additional revision lectures may be arranged in the module's 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.