Module ICE1221:
Mathematics for Computing
Module Facts
Run by School of Computer Science and Electronic Engineering
20 Credits or 10 ECTS Credits
Semester 1 & 2
Organiser: Dr David Evans
Overall aims and purpose
This module aims to provide:
 a grounding in computational thinking.
 an understanding of the fundamental mathematics underlying all computing.
 experience dealing with the mathematical structures underpinning programming and data structures.
 a revision of and practice with elementary algebra.
Course content
Indicative content includes:
 Introduce and use Graphs and Digraphs
 Apply Greedy Algorithms to various graphs
 Look at the fundamental of Sets and how to use them
 Learn to build Venn Diagrams
 Gain the concepts of Boolean algebra and Karnaugh maps to solve logic problems
 Use Lists in relation to algorithms
 Apply the use of Binary Trees for given problems
 Write Linear Difference Equations for various situations
 Gain a foundation in Differentiation both in theory and application.
Assessment Criteria
excellent
Equivalent to the range 70%+. Assemble critically evaluated, relevent areas of knowledge and theory to constuct professionallevel solutions to tasks and questions presented. Is able to crosslink themes and aspects to draw considered conclusions. Presents outputs in a cohesive, accurate, and efficient manner.
threshold
Equivalent to 40%. Uses key areas of theory or knowledge to meet the Learning Outcomes of the module. Is able to formulate an appropriate solution to accurately solve tasks and questions. Can identify individual aspects, but lacks an awareness of links between them and the wider contexts. Outputs can be understood, but lack structure and/or coherence.
good
Equivalent to the range 60%69%. Is able to analyse a task or problem to decide which aspects of theory and knowledge to apply. Solutions are of a workable quality, demonstrating understanding of underlying principles. Major themes can be linked appropriately but may not be able to extend this to individual aspects. Outputs are readily understood, with an appropriate structure but may lack sophistication.
Learning outcomes

Understand graph and digraph problems and algorithms for solving them.

Calculate probabilities for elementary and compound events.

Understand polynomial functions, and their derivatives.

Apply elementary algebra.

Perform basic matrix and vector operations.
Assessment Methods
Type  Name  Description  Weight 

COURSEWORK  Semester 2  Assignment 1  Matrix types, operations, vectors. Solving systems of equations using matrix techniques. 
12.5 
COURSEWORK  Semester 2  Assignment 2  Further Matrix methods. Probability Theory. 
12.5 
EXAM  Exam 1  Graph and set theory, algebra, Boolean Algebra and structures. 
25 
EXAM  Exam 2  Matrix, vector and probability. 
25 
COURSEWORK  Semester 1  Assignment 1  Graphs, polynomials and basic algebra 
12.5 
COURSEWORK  Semester 1  Assignment 2  Graphs, Calculus, Boolean Algebra, Advanced Algebra (including Determinant Theory) 
12.5 
Teaching and Learning Strategy
Hours  

Lecture  Traditional lectures (2hrs x 24 weeks). 
48 
Tutorial  Supporting tutorials (1hr x 24 weeks). 
24 
Private study  Tutordirected private study, including preparation and revision. 
128 
Transferable skills
 Numeracy  Proficiency in using numbers at appropriate levels of accuracy
 Computer Literacy  Proficiency in using a varied range of computer software
 SelfManagement  Able to work unsupervised in an efficient, punctual and structured manner. To examine the outcomes of tasks and events, and judge levels of quality and importance
 Information retrieval  Able to access different and multiple sources of information
 Critical analysis & Problem Solving  Able to deconstruct and analyse problems or complex situations. To find solutions to problems through analyses and exploration of all possibilities using appropriate methods, rescources and creativity.
Subject specific skills
 Solve problems logically and systematically;
 Knowledge and understanding of facts, concepts, principles & theories
 Problem solving strategies
 Development of general transferable skills
 Knowledge and/or understanding of appropriate scientific and engineering principles
 Knowledge and understanding of mathematical principles
 Knowledge and understanding of computational modelling
 Principles of appropriate supporting engineering and scientific disciplines
Courses including this module
Compulsory in courses:
 G400: BSC Computer Science year 1 (BSC/CS)
 G40B: BSc Computer Science (4 year with Incorporated Foundation) year 1 (BSC/CS1)
 G40F: BSc Computer Science year 1 (BSC/CSF)
 I103: BSc Computer Science with Game Design year 1 (BSC/CSGD)
 I102: BSc Computer Science (with International Experience) year 1 (BSC/CSIE)
 H113: BSc Data Science and Machine Learning year 1 (BSC/DSML)
 H114: BSc Data Science and Visualisation year 1 (BSC/DSV)
 H117: MComp Computer Science year 1 (MCOMP/CS)