This is an archived syllabus from 2017-2018

• View 2018-2019 syllabus.

COMP26120 Algorithms and Imperative Programming

Level 2
Credits: 20
Enrolled students: 240

Course leader: David Rydeheard

Additional staff: view all staff

Requisites

• Pre-Requisite (Compulsory): COMP16121
• Pre-Requisite (Compulsory): COMP16212

Additional requirements

• Students who are not from the School of Computer Science must have permission from both Computer Science and their home School to enrol.

  Alternative equivalent knowledge of Java accepted.

Assessment methods

• 50% Written exam
• 50% Practical skills assessment

Timetable

Semester	Event	Location	Day	Time	Group
Sem 1	Lecture	1.1	Thu	15:00 - 16:00	-
Sem 1 w2+	Lab	G23	Thu	09:00 - 11:00	F
Sem 1 w2+	Lab	G23	Tue	10:00 - 12:00	G
Sem 1 w2+	Lab	G23	Thu	11:00 - 13:00	H
Sem 2	Lab	LF31 + LF17	Fri	09:00 - 11:00	S
Sem 2	Lab	G23	Wed	10:00 - 12:00	R
Sem 2 w19-26,30-32,34	Lab	G23	Mon	10:00 - 12:00	Q
Sem 2	Lecture	1.1	Tue	14:00 - 15:00	-

Themes to which this unit belongs

• Programming and Algorithms
• Computer Languages

Overview

This is a two-semester practical introduction to algorithms and data structures, concentrating on devising and using algorithms, including algorithm design and performance issues as well as 'algorithmic literacy' - knowing what algorithms are available and how and when to use them.

To reflect the emphasis on practical issues, there are two practical (laboratory) hours to each lectured hour. Lectures serve to motivate the subject, orient students, reflect on practical exercises and impart some basic information. A range of practical applications of algorithms will also be presented in the lectures. Other information resources will be important, including a set textbook, which will provide essential support.

The course-unit starts with a 5-week primer on the C programming language, enabling students to become competent programmers in this language as well as in Java (and, possibly, in other languages). This teaching is supported by an on-line C course and extensive laboratory exercises.

There is a follow-up course unit on Advanced Algorithms in the Third Year. This presents the foundational areas of the subject, including (1) measures of algorithmic performance and the classification of computational tasks by the performance of algorithms, (2) formulating and presenting correctness arguments, as well as (3) a range of advanced algorithms, their structure and applications.

Aims

To make best use of available learning time by encouraging active learning and by transmitting information in the most effective ways.

To give students a genuine experience of C.

To make students aware of the importance of algorithmic concerns in real-life Computer Science situations.

To emphasise practical concerns, rather than mathematical analysis.

To become confident with a range of data structures and algorithms and able to apply them in realistic tasks.

Syllabus

C for Java programmers (up to reading week)

Algorithms - what they are and how to express them (in pseudocode and selected programming languages: Java and C).

Practical experience in devising, assessing and using algorithms:

• considerable practice in algorithmic problem solving for realistic problems
• examples from a wide range of application areas
• finding appropriate algorithms and data-structures
• inventing appropriate algorithms and data-structures

Practical experience in 'algorithmic literacy' - knowing how to use the extensive literature on the subject, recognising what algorithms to use in applications and assessing their utility.

A range of basic data structures: arrays, lists, trees (including ordered and balanced trees and heaps), and various kinds of graphs. Representations of basic data structures in programming languages.

A range of basic algorithms: searching and sorting algorithms, tree traversal and manipulation algorithms, some basic graph algorithms. Other algorithmic areas will be explored through practical examples.

An introduction to algorithmic performance: space and time requirements, worst-case, average-case and best case estimates. Practical experience and techniques for measuring and predicting performance: Counting operations.

Scaling and some common rates of growth.

Reasoning about algorithms - experience in informally reasoning about algorithms to establish correctness.

Teaching methods

Lectures

22 in total, 1 per week

Laboratories

44 hours in total, 22 2-hour sessions, 1 per week

Feedback methods

Study hours

• Assessment written exam (4 hours)
• Lectures (24 hours)
• Practical classes & workshops (48 hours)

Employability skills

• Analytical skills
• Innovation/creativity
• Oral communication
• Problem solving

Learning outcomes

Reading list

Additional notes

Course unit materials

Links to course unit teaching materials can be found on the School of Computer Science website for current students.