Bachelor of Software Engineering - Artificial Intelligence (AI)

This component introduces you to the fundamentals of creative enterprise, including entrepreneurship and the concept of an entrepreneurial mindset in the technology sector. It stimulates new ways of thinking about enterprising behaviour in a multi-disciplinary manner. You'll learn to identify opportunities, creatively solve problems, network, communicate persuasively and work effectively in a team. In addition, this component will empower you to propose new ventures that focus on social change for good.

This component introduces you to this rapidly growing field and equips you with some of its basic principles and tools as well as its general mindset. You'll learn concepts, techniques and tools you need to deal with various facets of data science practice, including data collection and integration, exploratory data analysis, predictive modeling, descriptive modeling, data product creation, evaluation, and effective communication. The focus in the treatment of these topics will be on breadth, rather than depth, and emphasis will be placed on integration and synthesis of concepts and their application to solving problems.

This component introduces you to core concepts of Networking and Database Systems. You'll learn the fundamentals of Database Management Systems and network topology, including network architecture. You'll be introduced to relational database models and learn the fundamentals of the structured query language (SQL). You'll then apply these concepts by completing multiple software engineering projects.

This component provides you with an opportunity to work collaboratively on a series of projects, enhancing skills such as project management, time management, prioritisation and a gamut of interpersonal skills within a team of people across multiple specialisations. Additionally, you'll be challenged to find creative solutions for product development and small-scale rapid prototypes. You'll engage in peer learning through agile development and processes. This learning experience will enhance your self-development and enable continuous learning.

This component helps you explore several important fields of general inquiry about significant intellectual issues related to human beings, so you can view everyday problems and formulate solutions in new ways. Human Centred Design gives you an appreciation of the factors that influence human behaviour and interactions, so that you can apply specialised skills to help solve problems affecting diverse societies.

This component builds on the skills and knowledge acquired from Concepts of Artificial Intelligence (AI). The component begins by exploring different classifications of AI (e.g., Expert Systems, Planning and Robotics, Natural Language Processing (NLP) and Speech Recognition, Machine Learning, and Computer Vision) and their current applications. You'll be presented with case studies focusing on the overview of the development of NLP, Speech Recognition and Computer Vision (most commonly used applications of AI and Machine Learning). This component also covers the AI for Good movement and how AI is being used to address economic and socially relevant problems.

This component introduces you to the statistical models for regression and classification necessary for more specialised components in this degree. The main topics covered are Classification Algorithms and Regression Algorithms; the practical use of both methods, how to evaluate the proposed models and how to choose between the different available methods. Theoretical lectures about the main concepts to be studied are followed by demonstrations of the different applications. Then you'll be asked to apply the learned concepts on different classification and regression problems.

In this component, students learn about the fundamentals of Cloud Application development. They acquire the skills to design and develop cloud solutions. Students learn about different cloud services such as Compute, Storage, Database among others, offered by key service providers. On completing this component students will have the ability to develop cloud-ready applications. They would also be able to deploy and monitor an application on the Cloud.

In this component, students will learn about advanced cloud computing techniques and services, cloud infrastructure prototyping for distributed systems and networks that enable cloud computing. Students explore different cloud networks and infrastructures including cost, availability, and scalability. Core techniques, algorithms, design philosophies, and distributed computing concepts will be introduced. The component will also cover the understanding of software-defined architectures and virtualisation.
Moreover, students will learn to analyse, discuss and deploy virtual networks on shared cloud infrastructure.

This component is designed to provide you with professional experience in an area related to your specialisation. The aim of providing industry-specific opportunities is to enable you to develop work-ready skills that will enhance your job prospects and build your career for the future. Much of the benefit of work integrated learning comes from observation, practising under supervision and reflection. Work Integrated Learning is an excellent way to broaden your learning environment while you're studying. It allows you to see first-hand how what you're learning in your degree translates into practice, as well as how ‘real world’ practice relates to what you're learning.

There are two work integrated learning options available:

Option 1: Internship/volunteer

Work within a technology company as an intern, or volunteer at technology non-profit. This encourages you to build long-term relationships with the tech industry and provides an opportunity for you to work with and learn from people who may end up becoming colleagues, bosses or mentors. It also provides a context in which to enhance your communication skills and work collaboratively in a professional arena. You'll undertake a series of industry-led tasks that are relevant to your field of study in order to understand the key concepts of working in and managing a professional technology team, with emphasis placed on the operation of the environment.

Option 2: Industry Live Brief

Respond to criteria set within the context of an Industry Live Project. An understanding of research methodologies appropriate to professional practice and the documentation of personal creative investigation will be explored. You'll also further investigate and examine entrepreneurial and commercial opportunities through collaborative work practice. The component is delivered from a cross-specialisation perspective and draws on both specific and common software engineering practices. You'll be required to work both independently and as part of a collaborative team in order to conduct research, analyse and define project parameters.

This component covers machine learning concepts and techniques such as supervised and unsupervised machine learning techniques; learning theory, reinforcement learning and model performance improvement. It also discusses risks and limitations of machine learning. The component will also introduce you to the applications of machine learning, such as robotics, data mining, computer vision, bioinformatics, and natural language processing.

This component requires you to have programming skills and knowledge in probability, statistics, regression, and classification.

This component extends students’ skills and knowledge learned in Machine Learning Principles and Applications of Artificial Intelligence. It discusses the application of statistical and other machine learning algorithms to analyse written and spoken language intelligently. It begins with a discussion of foundation concepts in natural language processing (NLP) and speech recognition such as language modelling, formal grammars, statistical parsing, machine translation, and dialogue processing. Students will then be presented with modern NLP and speech recognition quantitative techniques. Students will be working around different examples applying techniques and NLP toolkits

This component builds on the skills and knowledge students acquired from Machine Learning Principles and focuses on deep learning. It introduces you to foundational topics on neural networks, its applications to sequence modelling, computer vision, generative models and reinforcement learning. A focus will be given on learning how to model and train neural networks to implement a variety of computer vision applications. You'll be presented with practical examples of how to develop applications using deep learning. Knowledge in programming and understanding of machine learning concepts is required in this component.

In this component, students learn and review key best practices, and tool chains used to set up automated workflows for development and operations. Students increase their knowledge around DevOps and can minimise the manual tasks of code merge, code commits, branching, code reviews, builds, tests, code quality matrices, integration with the repository, analytics, and deployment. They also get an overview of scaling and monitoring across various environments.

This component teaches you data mining techniques for both structured and unstructured data. You'll be able to analyse moderate-to-large sized datasets, data preparation, handling missing data, modelling, prediction and classification. You'll also be able to communicate complex information in results of data analytics through effective visualisation techniques.

This component equips you with the fundamentals of Secure by Design, enabling an understanding of its underlying key principles and the core pillars of Information Security: Confidentiality, Integrity, and Availability. The component is structured around the main Secure Development Lifecycle (SDLC) models. It also discusses Security by Design principles, appropriate SDLC model selection, application of secure development techniques, vulnerabilities and techniques to tackle them, secure design and development best practices, introduction to encryption, and introduction to the classification of security flaws and application security.

In this component, students learn about scaling and monitoring processes in the DevOps Environment. Students familiarise themselves with associated tools and can set them up. They learn about the services, which can be monitored to ensure maximum availability of the application. Students also learn to scale the environment on demand. They develop the skills to deliver software which is scalable, reliable, available and manageable.