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COMP4197: RANDOMISED ALGORITHMS AND PROBABILISTIC METHODS

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Type Open
Level 4
Credits 10
Availability Available in 2024/2025
Module Cap None.
Location Durham
Department Computer Science

Prerequisites

  • COMP2181 Theory of Computation

Corequisites

  • None

Excluded Combinations 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

ActivityNumberFrequencyDurationTotalMonitored
lectures222 per week1 hour22 
preparation and reading78 
total100 

Summative Assessment

Component: ExaminationComponent Weighting: 100%
ElementLength / DurationElement WeightingResit Opportunity
Examination2 hours100No

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.

More information

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