Module Catalogue 2025/26

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

Knowledge of a range of key application areas where algorithmic solutions are required.
Understand the key issues in algorithm design.
Understand what makes a "good" algorithm.
Explore a range of techniques for algorithm design.
Ability to analyse an algorithm’s complexity.

Outline Of Syllabus

Introduction to Algorithms:
•       Introduce the idea of an algorithm
•       Documenting an algorithm and the use of pseudo code
•       Introduction to algorithm analysis

Fundamental Algorithmic Problems:
•       Searching
•       Sorting
•       String searching
•       Graph problems

Algorithm Analysis:
•       Asymptotic analysis of upper and average complexity bounds
•       Identifying differences among best, average, and worst case behaviours
•       Standard complexity classes
•       Using recurrence relations to analyze recursive algorithms
•       NP Complete problems

Algorithm Design Techniques:
•       General ideas for algorithm development
•       Brute-force algorithms
•       Greedy algorithms
•       Divide-and-conquer
•       Backtracking

Intended Knowledge Outcomes

By the end of this module students will have gained the knowledge to:
•       Identify a key range of algorithm application areas and their associated algorithms.
•       Recognise and define what makes a good algorithm.
•       Experience of a range of techniques for developing algorithms.
•       Understand different approaches used to analyse algorithms.
•       Understanding of NP complete problems.

Intended Skill Outcomes

By the end of this module students will be able to apply their knowledge and understanding of the topics covered to:

•       Be able to read and understand algorithmic descriptions.
•       Be able to trace the behaviour of an algorithm.
•       Design and document their own algorithms.
•       Analyse the complexity of algorithms.
•       Evaluate the appropriateness of an algorithmic solution to a problem.

Teaching Rationale And Relationship

Lectures (PiP) and online study videos will be used to introduce the key module material and for demonstrating the key concepts by example. Students are expected to follow-up lectures within a few days by re-reading and annotating lecture notes to aid deep learning.

Tutorial exercises will be provided online and integrated into the study materials. These allow students to gain practical skills and understanding in the theory and techniques covered.

There will be a series of tutorial sessions (PiP) that will look at applying the techniques and skills developed in problem solving situations. Online quizzes will be used at key points in the module to help student check and reinforce their progress.

Assessment Rationale And Relationship

The digital exam assessment given at the end of the module ensures students get the vital opportunity to go back over the material covered (this is important given the type of technical topics considered) and is very well suited to formally assessing the type of material covered in this module.

A range of formative assessments are used to support student’s self-study during the module and gauge their understanding as the course progresses.

General Notes

N/A