Computer Science

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 will develop your programming skills further through the production of games using industry-standard game engines and tools.   

You’ll study topics such as: 

• Game development concepts, processes, mathematics, and techniques 

• Sprite sheets, animation, lighting, physics, collision detection, mathematical vectors, and quaternions 

• Game development tools: game engines, image editing software, and audio editing software 

• Development of 2D and 3D games 

This module develops your ability to differentiate and choose between different operational database systems for daily operations or simple analytics and use of distributed systems to manage data on a larger scale. It will develop your understanding of relational and non-relational (NoSQL) databases, and how to program a NoSQL database Distributed System.  You will get practical experience of storing, manipulating, and retrieving semi-structured data and perform aggregation functions with big datasets used in cloud computing. 

You’ll study topics such as: 

The module will advance on from the database module completed in the first year. Concepts covered at this stage will include: 

• Data management and database management 

• Database system architectures 

• Relational data model 

• Handling simple and complex data types 

• Visualisation of data 

• Database integrity, security, encryption, use of constraints 

• Business driven data strategies 

• Cloud-based data handling tools 

• NoSQL 

This module introduces you to mobile application development.  It will explore the capabilities of modern devices for you to create highly interactive mobile applications, using industry standard software development kits (SDKs). 

You’ll study topics such as: 

• User interface components 
• Layouts and styles 
• Navigation 
• Controls and lists 
• Mobile application features: sound effects, notifications, gestures, camera/photo gallery, geo-location and maps integration 
• App lifecycle 
• Device storage procedures and access APIs 
• Architectural patterns for mobile application development 
• Authentication 
• Mobile application deployment 

This module is for undergraduate students to study abroad in their second year, Semester 2 (only for courses that offer this option). With this module, you can spend a semester at one of the University’s approved partner institutions worldwide – from Europe to the Americas, Australia or Canada.  

Study Abroad plays an important role in the University's commitment to an engaging, challenging, and thriving learning culture. It offers opportunities to experience other academic cultures and foster intellectual maturity while enhancing co-curricular skills and students' long-term employability. 

Study abroad for credit is permitted on existing university-approved courses only. Students are awarded credits and grades at the partner institution, which are converted into Sheffield Hallam credits and grades on return and included in the Sheffield Hallam degree classification. 

Please check and refer to the webpage How study abroad works. You must submit a Learning Agreement outlining the modules you will be taking at the partner institution. The Learning Agreement will be signed off by your academic tutor to ensure that the Learning broadly covers the Learning Outcomes set out in your course curriculum during your study abroad.   

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 an opportunity to work as a software engineer, work out an innovative solution for a problem and define a software engineering method to develop the solution. You’ll focus on the end user’s functional pain points through a design thinking workshop, deciding on a software engineering methodology influenced by industry-standard methods. 

You’ll study topics such as: 

• Pain points and requirements using design thinking  

• Technical solutions leveraging reference architectures 

• Defining software engineering method 

• Industry-standard practice-oriented methodology like IBM Garage Method 

• MVP-driven ‘envisage co-create build-at-scale’ development 

• Optimising software engineering methodology with techniques like LEAN 

• Understanding organisation maturity in line with SEI/CMM 

As hardware becomes more complex and as the problems which computing addresses scale larger we are finding the limits of popular abstractions. In particular object-orientation is being stretched to breaking point when programs are executed on massively concurrent or parallel architectures. Some people are finding that functional languages give them the flexibility to handle scale and concurrency effectively. As a side-effect they often find that their functional programs are simpler and more readable than OO versions.

This module will introduce students to functional programming. They will learn one of the popular languages (probably Scala or Erlang) and see how it can be applied to a variety of data-intensive domains. Along the way you'll explore some of the data manipulation techniques which came out of research into Artificial Intelligence (AI).

INDICATIVE CONTENT

• Introduction to functional ideas - why functional programming
• What are functions?
• Treating program state as a homologue of data
• Lambda expressions
• Provability - how do we know what result our program will produce?
• Using a functional language (Scala/Erlang/Clojure/F#)
• Applying artificial intelligence to manipulate state

This module explores some of the important applications we use – from Google and Amazon to the many social media platforms – which have built their utility and success on top of machine learning systems. Using Python programming and a range of libraries, you’ll learn to understand recognition, prediction, rankings, recommendation systems, social bookmarking and more. 

You’ll study topics such as: 

• Machine learning in AI, social media, and web applications 

• Python libraries for machine learning 

• Recommendations based on the ‘likes’ of individuals and groups 

• Using neural networks to learn about computer vision tasks 

• Ranking search results based on the context they were created in 

• Building pricing models using a variety of techniques 

• Building systems whose intelligence evolves using genetic algorithms 

This module explores the recent progression of ‘electronics’ from the design and development of circuitry towards programmable devices which receive input from sensors, using that information in various ways to ‘control’ outputs and produce effects. This emerging ‘internet of things’ and ‘physical computing’ is seen in Arduino boards, robots, smart homes and embedded devices. 

You’ll study topics such as: 

• Prototyping tools, processes and methods 

• Physical computing system controller, motors and sensors  

• Embedded robotic devices 

• Handling real-time data 

• Control algorithms 

This module introduces the practice of secure software engineering, exploring the main objectives of security in the construction of large complex applications. We’ll view historical cases of software failure to demonstrate the main problems and implications for poor security practices in software development, which you’ll use as motivation for the study of current software and security engineering practices. 

You’ll study topics such as: 

• Information security concepts, objectives and properties  

• Confidentiality, integrity availability  

• Security in the software development lifecycle 

• Cryptography foundation and application 

• Digital identity management and access control  

• Identification, authentication, authorisation and audit  

• Vulnerabilities, threats and attacks 

• Protection and defence mechanisms and tools