# 2021/22 Taught Postgraduate Module Catalogue

## OCOM5102M Algorithms

### 15 creditsClass Size: 100

Taught: 1 Jan to 28 Feb, 1 Jan to 28 Feb (adv year), 1 Jul to 31 Aug View Timetable

Year running 2021/22

This module is not approved as an Elective

### Module summary

Algorithms and algorithmic problem solving are at the heart of computer science. This module introduces students to the design and analysis of efficient algorithms and data structures. Students learn how to quantify the efficiency of an algorithm and what algorithmic solutions are efficient. Techniques for designing efficient algorithms are taught, including efficient data structures, standard methods such as Divide-and-Conquer and Dynamic Programming as well as more advanced techniques for computationally intractable problems and large data sets. This is done using illustrative and fundamental problems relevant to AI.

### Objectives

The aims of this module are to enable students to:

- appreciate and apply algorithmic thinking

- appreciate what constitutes an efficient and an inefficient solution to a computational problem;

- identify and apply design principles such as greediness, divide and conquer and dynamic programming;

- analyse and implement some fundamental algorithms;

- describe efficient algorithms for fundamental computational problems, along with their computational complexity.

- understand the difference between polynomial and exponential time algorithms;

- know how NP-hard problems can be dealt with in practice;

- appreciate selected cutting-edge modern algorithms;

- articulate the key concepts and justify approaches in a clear and rigorous manner

Learning outcomes
On completion of this module students should be able to:

1. Demonstrate an understanding of what constitutes an efficient and an inefficient solution to a computational problem

2. Analyse the efficiency of algorithms

3. Evaluate and justify appropriate ways to provide efficient solutions for computational problems

4. Identify and apply design principles such as greediness, divide and conquer and dynamic programming in the design of efficient algorithms

5. Describe efficient algorithms for a range of computational problems, along with their computational complexity

6. Articulate the key concepts and critically evaluate approaches in a clear and rigorous manner

### Syllabus

Indicative content for this module includes:

- Algorithmic thinking (the stable matching problem)

- Basic tools: logic, graphs and networks, mathematical reasoning

- Time and space complexity, asymptotic analysis of algorithms

- Algorithms: stable matching, graph searching, minimum spanning trees, shortest path,
sorting, interval scheduling

- Algorithm design principles: Greedy algorithms, divide and conquer,

dynamic programming; branch and bound;

- Fundamental data structures

- Intractability: the classes P and NP

- Dealing with NP-hard problems in practice

- Markov chains: computing the page rank (probability and matrices)

- Approximation algorithms

### Teaching methods

 Delivery type Number Length hours Student hours On-line Learning 6 1.00 6.00 Group learning 6 2.00 12.00 Independent online learning hours 28.00 Private study hours 104.00 Total Contact hours 18.00 Total hours (100hr per 10 credits) 150.00

### Private study

Private study will include directed reading and exercises and self-directed research in support of learning activities, as well as in preparation for assessments.

Private study will include directed reading and exercises and self-directed research in support of learning activities, as well as in preparation for assessments.

Independent online learning involves non-facilitated directed learning. Students will work through bespoke interactive learning resources and activities in Minerva.

### Opportunities for Formative Feedback

Online learning materials will provide regular opportunity for students to check their understanding (for example through formative MCQs with automated feedback). Regular group activity embedded into learning will allow self and peer assessment providing opportunities for formative feedback from peers and tutors.

### Methods of assessment

Coursework
 Assessment type Notes % of formal assessment In-course Assessment Report 20.00 In-course Assessment Report 20.00 In-course Assessment Online time-limited assessment 60.00 Total percentage (Assessment Coursework) 100.00

This module will be reassessed by an online time-constrained assessment.