Undergraduate Programme and Module Handbook 2024-2025
Module COMP4197: RANDOMISED ALGORITHMS AND PROBABILISTIC METHODS
Department: Computer Science
COMP4197: RANDOMISED ALGORITHMS AND PROBABILISTIC METHODS
| Type | Open | Level | 4 | Credits | 10 | Availability | Available in 2024/2025 | Module Cap | None. | Location | Durham |
|---|
Prerequisites
- COMP2181 Theory of Computation
Corequisites
- None
Excluded Combination of Modules
- 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 | |
|---|---|---|---|---|---|
| lectures | 22 | 2 per week | 1 hour | 22 | |
| preparation and reading | 78 | ||||
| total | 100 |
Summative Assessment
| Component: Examination | Component Weighting: 100% | ||
|---|---|---|---|
| Element | Length / duration | Element Weighting | Resit Opportunity |
| Examination | 2 hours | 100% | No |
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.
■ 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