Computer Science with Artificial Intelligence with Foundation Year

This module introduces basic computing knowledge and skills to a level that will give you confidence you can study and practice computing at Honours level.   

Indicative Content 

• Essential concepts of computer programming  
• Database queries 
• Web technologies   
• Basics of how computers and computer networks operate    
• Issues of Security 
• Awareness of own pace of learning, and how to learn effectively. 

The focus of the module is on helping you gain a well-rounded understanding of computing, on able to see common threads in development methods, and being able to apply your learning about computing, and your learning about study approaches into project work on other areas on the course. 

This module extends on “Foundations in Computing 1” (and has study of that module as a prerequisite). You will explore computing to a greater depth - for example further program design and implementation, and explore wider uses of computing used in business, social contexts and embedded into other technologies. . 

Indicative Content:

• Programming simple programs effectively 

• Understanding example application areas (e.g. IoT, Games) 

• Understanding how computing supports businesses 

• Further practise of skills in a topic area you are considering selecting for Honours studies 

The module maintains the ethos of its prerequisite (Foundations in Computing 1), by provide you with a broad but largely integrated understanding of computing. A further strong aim is to help you make a properly informed choice as to which branch(es) of computing you wish to follow for study at Honours level. 

This module develops your mathematics skills relevant to the IT discipline. You choose whether to target an “Advanced” level (which includes topics relevant to degrees in the Computer Science area), or “Standard” level (which includes topics that can occur in any IT-related studies). 

You’ll study topics such as: 

• Fractions and decimals 

• Percentages, rounding, estimations 

• Standard form, metric units, indices, powers 

• Algebra and solving linear equations 

• Factorisations 

• Matrices 

• Logic gates and truth tables 

• Binary number system 

• Statistics 

Indices and powers (advanced level) 

• Factorising and solving quadratic equations (advanced) 

• Simultaneous equations and matrices (advanced) 

• Correlation and linear regression (advanced) 

This module is specifically designed to help support students on the Foundation Year of a BSc Honours course in a computing area.  You will develop an understanding of, and practise, skills needed for study at Honours level and for eventual employment. 

Indicative Content 

• Disability and Inclusiveness 

• Understanding learning processes and its relationship to success in assessment 

• Finding appropriate information 

• Effective communication 

• Effective use of IT Tools for clear communications and personal professional organisation 

• Academic Integrity 

An underlying theme of the module is to help you gain an ability to see for yourself where your study skills lie, and which areas you can make further improvements.  

This module will introduce concepts of discrete mathematics and numerical skills related to concepts for application in computer science. The module is designed to give students the skills necessary to evaluate systems and data numerically and to apply rigorous mathematical thinking to the modelling of those systems and data. You’ll study topics such as: · Sets, relations, and functions · Propositional and predicate logic · Cardinality, combinatorics, recurrence, and modular arithmetic · Proof techniques: structure, induction, counterexamples, contradiction · Graphs and trees · Discrete probability · Data organisation: data structures, e.g., arrays, lists and trees

· Matrices · Mathematics of cryptography · Hexadecimal and binary number systems · Floating point and other number systems · Boolean algebra, truth tables, de Morgan’s Law

This module provides an introduction to computer programming. It will provide a foundation and understanding of key programming concepts and their deployment in a mainstream object-oriented programming language. The focus is on the design and implementation of programs and algorithms using the imperative facilities of a programming language, which is widely used for developing modern applications.  This will extend into basic concepts of object-oriented programming. 

The module offers an opportunity to acquire practical skills that are fundamental to BSc Computer Science and constitutes a levelling experience that acknowledges the fact that learners entering the programme possess a wide variety of programming skills and experience.  Teaching materials for this module will include classical Computer Science topics and concepts. 

By the end of the module, students should be able to construct simple imperative and object-oriented applications from only basic descriptions using a combination of programming experience and problem-solving. 

Indicative Content 

• Problem solving, top-down design and functional decomposition 

• The development process - specification, design, implementation, testing. 

• Variables and Data Structures 

• Algorithms and Control Structures 

• Program Quality 

• Software tools and Program Development Environments 

This module uses real projects for you to apply and continue to develop your software development knowledge against.  It will support the development of your collaborative skills in a software development environment alongside relevant Legal, Social, Ethical and Professional Issues (LSEPI) and their impact within the IT and software industry. 

You’ll study topics such as: 

• User requirements & user stories 

• Acceptance testing 

• Software prototyping and fidelity of prototypes 

• Software development process and lifecycles 

• Use case modelling 

• Database design 

• Object-oriented design 

• User interface design 

• Legal, Social, Ethical and Professional issues in IT 

• Software testing 

• Sustainable development and deployment 

• Data security 

This module will continue to build your programming knowledge, looking at object-oriented programming in a modern programming language such as C/C++.  It will introduce manual dynamic memory management and discuss heap and stack memory alongside profiling.  You will practice more advanced aspects of programming languages and develop an understanding of design patterns and standard software libraries. 

You’ll study topics such as: 

• Object-oriented principles, concepts and design: encapsulation, inheritance, polymorphism 

• Abstract and interface classes 

• Dynamic memory management 

• Memory pointers and references 

• Memory profiling 

• Standard template libraries (STLs) 

• Operator overloading 

• Templates / generics 

• Event-based coding 

• Design patterns 

• Tool support for OO development  

• Version control software  

Aims: 

This module will impart key concepts in computer science to students and to further develop important skills in computer-based problem-solving and data manipulation.  

Indicative Content  

• Introduction to algorithms (role and importance of algorithms in computer science). Concurrent, and parallel algorithms 
• Sorting algorithms (merge sort, heap sort, quick sort) 
• Searching algorithms 
• Linked lists (Singly linked lists; double linked lists) 
• Stacks and Queues 
• Recursion  
• Tree structures (creating, searching, traversing, merging). 
• Graph structures (traversals, activity networks, critical paths, shortest paths) 
• The theory of algorithms (Big-O notation, Computability; Turing machines). 
• Complexity (computational and control flow). 
• Optimisation problems (Travelling Salesman Problem, Bin Packing). 
• Optimisation search methods (Genetic algorithm, Hill Climbing, Simulated Annealing, Tabu search algorithm, Iterated Local Search).  

This module delivers knowledge and practical skills of machine learning algorithms for artificial intelligence applications. It will present the key mathematical principles and concepts required to create machine learning algorithms through data-driven approaches. You will gain practical experience while designing, implementing, and evaluating machine learning systems to solve real-world artificial intelligence problems. 

You’ll study topics such as: 

• Data-driven approaches to machine learning 

• Linear regression and gradient descent  

• Regression and classification problems  

• Machine learning algorithms: decision trees, support vector machines, ensemble learning and k-mean/mean-shift clustering 

• Data pre-processing, cleansing and feature extraction  

• Evaluating machine learning system by using cross-validation, confusion matrix and receiver operating characteristic 

This module provides a platform for you to work collaboratively in small teams to build complex software applications relating to industry.  You’ll typically work with external clients on the projects, where you will also continue to develop your professional practice and profile, that can be used to support placement opportunities. 

You’ll study topics such as: 

• Modern software development methodologies 

• Effectively team working  

• Version control tools  

• Architecture of CRUDing applications  

• Building secure APIs  

• Testing strategies for software development  

• Cloud deployment technologies  

• Professional profile: CVs, covering letters, portfolio, and reflective practice 

• Building secure and sustainable systems 

• Accessible and culturally appropriate software 

Module aim:    

The aim of this module is to enhance students’ professional development through the completion of and reflection on meaningful work placement(s).  

A work placement will provide students with opportunities to experience the realities of professional employment and experience how their course can be applied within their chosen industry setting. The placement will: 

• Allow student to apply the skills, theories and behaviours relevant and in addition to their course 
• Enable students to enhance their interpersonal skills in demand by graduate employers – communication, problem-solving, creativity, resilience, team work etc.  
• Grow their student network and relationship building skills. 
• Provide student with insights into the industry and sector in which their placement occurs 
• Help student make informed graduate careers choices. 

Indicative Content:  

In this module students undertake a sandwich placement (min 24 weeks / min 21 hours per week) which is integrated, assessed and aligned to their studies.  

Their personal Placement Academic Supervisor (PAS) will be their key point of contact during their placement and will encourage and support students to reflect on their experience, learning and contribution to the organisation they work for.  

To demonstrate gains in professional development, students will be required to share their progress, learning and achievements with their Placement Academic Supervisor and reflect on these for the summative piece of work. 

Machine Learning techniques, such as artificial neural networks, are widely used in modern intelligent systems. The module aims to deliver key concepts, theories and practical skills to implement state-of-the-art neural networks techniques within the artificial intelligence problems domain. This module enhances the knowledge and experiences from corresponding level 5 machine learning modules and focuses on smart system design, development, evaluation and optimisation.

Indicative Content

• Artificial and Deep Neural Networks

• Algorithms of training Neural Networks

• Customising pre-trained models and transfer learning

• Training large scale neural networks through GPUs, multiple devices and cloud computing

• Computer vision tasks with convolutional neural networks

• Natural language processing with recurrent neural networks

• Reinforcement learning algorithms

• Generative learning algorithms

This module is a research project of your choice – you’ll identify a computer-based problem, investigate the requirements, analyse results of research undertaken and design, and develop and evaluate a solution to that problem. You’ll then evaluate the project’s success, your learnings and opportunities for further work. 

You’ll apply skills and learning such as:

• Ideation and planning a larger-scale project 
• Information gathering and literature reviews 
• The selection of tools, techniques or methods 
• Implementation, testing and user evaluation 
• Critical reflection on project deliverables, success or failure 
• Referencing and citation techniques 
• Legal, social, and ethical considerations  
• Security and confidentiality 
• Sustainable development and deployment 
• Employability skills and attributes 

The aim of this module is to give you the opportunity to apply the principles of good design, usability, and security to the development of software. It does so by exploring a case study that forms the starting point for discovery, discussion and development. In adopting a practical approach, different design techniques and architectural styles can be used to develop solutions that are then evaluated against industry-standard criteria using both qualitative and quantitative techniques. 

Indicative Content 

• Gathering requirements 

• User-Centred Design (including aspects of inclusive design with a particular focus on assistive technology). 

• Designing a solution architecture 

• Selecting and implementing design patterns. 

• Understanding anti-patterns and code smells. 

• Evaluating software architecture and quality characteristics. 

• Understanding the role of measurement and modelling in the design of systems. 

• The relationship between frameworks, design patterns and architectures. 

• Implementation of software using frameworks. 

• Software and user testing. 

• Testing the security of a system 

• The importance of understanding of culture and inclusivity in the design of systems