FOUNDATIONS OF COMPUTING II - 2023/4
Module code: COM1033
Module Overview
The course builds upon COM1026, Foundations of Computing, and introduces the key concepts of linear algebra and multivariate calculus.
Module provider
Computer Science and Electronic Eng
Module Leader
NERI Ferrante (CS & EE)
Number of Credits: 15
ECTS Credits: 7.5
Framework: FHEQ Level 4
Module cap (Maximum number of students): N/A
Overall student workload
Independent Learning Hours: 80
Lecture Hours: 24
Tutorial Hours: 24
Guided Learning: 10
Captured Content: 12
Module Availability
Semester 2
Prerequisites / Co-requisites
None
Module content
Indicative content includes:
- Linear algebra:
- Vectors, matrices and basic operations
- Rank of matrices, systems of linear equations
- Vector spaces
- linear mapping
- Calculus:
- Review of one-variable calculus
- Functions of two variables
- Lagrange multipliers, steepest descent.
- Basic integration in two dimensions.
Assessment pattern
| Assessment type | Unit of assessment | Weighting |
|---|---|---|
| School-timetabled exam/test | Invigilated class test (2hrs) | 50 |
| Examination | Invigilated exam (2hrs) | 50 |
Alternative Assessment
N/A
Assessment Strategy
The assessment strategy is designed to provide students with the opportunity to demonstrate that they have achieved the module learning outcomes.
Thus, the summative assessment for this module consists of:
· A 2-hour in-class test examining the linear algebra contents.
· A 2-hour examination covering multi-variate calculus.
Formative assessment and feedback
EVS handsets may be used extensively in the lectures, with each lecture consisting of a number of slides explaining the theory followed by a number of slides gauging the students’ understanding. The answers are discussed when necessary, eg if a high proportion (more than 25%) of the students get the answer wrong. Individual formative feedback will also be given during the lab sessions and as part of the summative assessment.
Module aims
- This module aims to deepen the students' understanding of mathematical functions, including linear algebra and calculus, and their applications, and demonstrate how these are relevant to the discipline.
Learning outcomes
| Attributes Developed | ||
| 001 | Understand vectors, matrices and perform linear algebra basic operations, determinants and rank of a matrix. | KCT |
| 002 | Solve systems of linear equations and understand the theory behind them. | KCT |
| 003 | Understand the concepts of vector spaces, operations over them, linear span, bases, and manipulate them. | KCT |
| 004 | Understand the concepts of linear mappings, kernel, image, eigenvalues, eigenvectors and eigenspaces. | KCT |
| 005 | Understand partial derivatives and multi-dimensional integrals | KCT |
| 006 | Be able to understand and apply the concepts in the context of computer science. | CPT |
Attributes Developed
C - Cognitive/analytical
K - Subject knowledge
T - Transferable skills
P - Professional/Practical skills
Methods of Teaching / Learning
The learning and teaching strategy is designed to:
- Help students understand dimensionality and be confident in manipulating vectors and mathematical functions of vectors
- Practise solving real-world problems by translating them into mathematical expressions
The learning and teaching methods include:
- Lectures (12 weeks at 2h)
- Tutorial session (12 weeks at 2h)
Indicated Lecture Hours (which may also include seminars, tutorials, workshops and other contact time) are approximate and may include in-class tests where one or more of these are an assessment on the module. In-class tests are scheduled/organised separately to taught content and will be published on to student personal timetables, where they apply to taken modules, as soon as they are finalised by central administration. This will usually be after the initial publication of the teaching timetable for the relevant semester.
Reading list
https://readinglists.surrey.ac.uk
Upon accessing the reading list, please search for the module using the module code: COM1033
Other information
Digital Capabilities
The practical lab components of this module explore how computers can be used to reason about, model and solve mathematical problems. It teaches the maths required to understand important computing concepts taught later in the course and is used throughout the later artificial intelligence modules.
Employability
This module provides foundational maths, skills that allow students to reason about complex computer science problems and apply mathematical techniques to solve real life problems. Topics such as calculus and algebra are widely applicable in a range of different domains and the theoretical and practical skills taught in this module are valued in industry.
Global and Cultural Skills
Mathematics is a global language and the foundational knowledge taught in this module can be applied throughout the world.
Resourcefulness and Resilience
This module involves practical problem-solving skills that teach a student how to reason about and solve new unseen mathematical problems through applying the foundation theory taught in this module. The practical components of this module teach students to use their computer science knowledge to explore solutions unseen mathematical problems
Programmes this module appears in
| Programme | Semester | Classification | Qualifying conditions |
|---|---|---|---|
| Computing and Information Technology BSc (Hons) | 2 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
| Computer Science BSc (Hons) | 2 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
| Computer Science MEng | 2 | Compulsory | A weighted aggregate mark of 40% is required to pass the module |
Please note that the information detailed within this record is accurate at the time of publishing and may be subject to change. This record contains information for the most up to date version of the programme / module for the 2023/4 academic year.