Bachelor Of Software Engineering (Artificial Intelligence)

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For over a decade, Media Design School has been a reliable source of high calibre graduate talent. Their pro-activeness in involving industry and keeping their curriculum up to date means graduates leave with commercially relevant skills and knowledge, and their inclusion of team based project opportunities trains students in the collaborative abilities they'll need in the modern workplace
Mario Wynands, Founder and Managing Director of PikPok

Study a Bachelor of Software Engineering (Artificial Intelligence) and become a highly sought-after developer with a deep understanding of algorithms and techniques used in solving problems of natural language processing, computer vision and more. You’ll explore different models for pattern recognition, use them in cloud environments, master the fundamentals of machine learning, and much more. And, to ensure that you leave with an industry-leading qualification, Media Design School have partnered with IBM to design a course that will arm graduates with the technical acumen and core soft skills required for a successful career in AI. It has been predicted that AI will be the most significant change driver over the next two decades, with 2.3 million jobs expected to appear as early as 2020*, and industry feedback indicates a significant shortage of employees demonstrating both technical prowess and an ability to think critically and creatively. This course covers a range of technical subject areas with a focus on the major areas of AI – Computer Vision, Natural Language Processing, Speech Recognition, and Machine Learning & Robotics. A soft skills thread is carried throughout the programme, to ensure students also leave with the in-demand skills of ideation, design thinking, project and time management and interpersonal communication. *(Gartner)”

Key Study Outcomes:

  • Awareness of the software engineering body of knowledge with a view towards ethics of practice in a global and sustainable context.
  • Apply creative skills to identify and solve complex commercial software engineering problems
  • Communicate proficiently in the context of software engineering across a variety of audiences utilising appropriate tools and interfaces.

Course Delivery

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Workload and Assessment

No. of timetabled hours per week:

Each subject involves 10 hours of study per week, comprising 4 timetabled study hours and 6 personal study hours.

Typical assessment includes:

Practical assignments, research projects, presentations and reports

Subject Information

The goal of this subject is to familiarise the student with the basic concepts of artificial intelligence and the problems AI is used to solve. The course content is organised around the three main areas of AI: Search, Logic and Learning. Topics covered include basic search, heuristic search, adversarial search, constraint satisfaction, logical agents, logic and inference, knowledge representation, probabilistic reasoning, knowledge in learning, learning probabilistic models, reinforcement learning and ethics of AI.

Students are introduced to an object oriented programming language and when they have mastered basic programming skills they move on to constructing simple projects. They begin by solving easy problem-based tasks with OOP and progress on to learn how to construct, test, and debug simple programs. Lecturers provide modern theoretical perspectives and demonstrate approaches to the tasks with examples.

This subject introduces students to foundational mathematical concepts necessary for specialisation subjects in their degree. Main topics covered are – Linear Algebra, Discrete Maths and Geometry. The delivery consists of theoretical elements, a demonstration, and then the lecturers allow students to put these skills into practice. The students collaborate and share mathematical problem-solving approaches during frequent in-class discussions and are expected to provide these solutions for class reviews.

This subject provides an elementary introduction to probability and statistics with applications.
In probability, students will learn about probability and distribution theory by defining probability and then studying its key properties. The subject will also introduce concepts of random variables, outcomes of random experiments and data analysis techniques using the statistical computing package R or SPSS.

In statistics, students will study data and uncertainty. Students will learn how to use statistics in the design of effective experiments and in determining the type of data collected. Underlying these techniques is the assumption that these data are samples of a random variable that follows a probability distribution describing their behaviour.

Students learn the fundamental data structures and algorithms that are needed to solve common software engineering problems. Students improve their learning throughout this subject by working on a large number of projects. They solve common problems by designing, developing, implementing, testing, and enhancing a collection of data structures and algorithms.

In this subject students learn the fundamentals and core concepts of Service Oriented Architecture and characteristics of microservices. They compare microservice architecture with monolithic style, emphasising why the former is better for continuous delivery. They also deal with operational complexities that are created while managing, monitoring, logging and updating microservices, and learn about the tools used to successfully manage, deploy and monitor applications based on microservice.

The aim of this subject is to provide students with fundamental knowledge of data, questions, and tools that a data scientist deals with. Students will not only be introduced to the ideas behind turning data into information but will also be introduced to the data scientist’s toolbox. Topics include: data scientist skills and responsibilities in a business including planning, performing and presenting projects; data science code of ethics; data manipulation tools and techniques.

This subject introduces students to core concepts of Networking and Database Systems. Students learn fundamentals of Database Management Systems and network topology including network architecture. They are introduced to relational database models and learn fundamentals of structured query language (SQL). Students will apply these concepts through completing multiple software engineering projects.

This subject builds on the skills and knowledge students acquired from Concepts of Artificial Intelligence (AI). The subject 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. Students will 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 subject also covers the AI for Good movement and how AI is being used to address economic and socially relevant problems.

Students are introduced to the fundamental topics of core computer graphics, 3D graphics programming and the rendering pipeline. Topics included are the transformation pipeline, device states, primitive rendering, basic camera systems, lighting, texturing, alpha techniques as well as software engineering design principles and testing strategies. By the end of the subject, students create a game utilizing 3D graphics concepts as introduced in the class.

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

This subject helps students explore several important fields of general inquiry pertaining to significant intellectual issues related to human beings so they can view everyday problems and formulate solutions in new ways. Broadly, the subject covers the theory of knowledge, human cognition, ethical and moral values, analysis of human history, critical analysis, appreciation of literature and arts and social interaction among human beings through a technological context. Human Centered Design is to give students an appreciation of the factors that influence human behavior and interactions so that they can apply specialised skills to help solve problems that affect diverse societies.

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

This subject introduces students to the statistical models for regression and classification necessary for more specialised subjects 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 the students are asked to apply the learned concepts on different classification and regression problems.

The aim of this subject is to teach students data mining techniques for both structured and unstructured data. Students will be able to analyse moderate-to-large sized datasets, data preparation, handling missing data, modelling, prediction and classification. Students will also be able to communicate complex information in results of data analytics through effective visualisation techniques.

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

This subject requires students to have programming skills and knowledge in probability, statistics, regression, and classification.

This subject is designed to provide students with professional experience in an area related to their specialisation. The aim of providing industry-specific opportunities is to enable students to develop skills that will enhance their prospects of gaining meaningful employment and building their career for the future.
Much of the benefit of work integrated learning comes from observation, practicing under supervision and reflection. Work Integrated Learning is an excellent way to broaden the students learning environment while they are studying. It allows them to see first-hand how what they are learning in their degree translates into practice, as well as how ‘real world’ practice relates to what they are learning at University.
This subject will develop work ready skills and boost students’ employability while they are studying.

There are two work integrated learning options available to students:
Option 1: Industry Placement
Students are offered the opportunity to work within a technology company as an intern or volunteer at a technology non-profit organisation. It encourages students to build long-term relationships with the tech industry and provides an opportunity for them 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 their communication skills and work collaboratively in a professional arena. Students will undertake a series of industry-led tasks that are relevant to their 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
This subject requires students to 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. Students will also further investigate and examine entrepreneurial and commercial opportunities through collaborative work practice. The subject is delivered from a cross specialisation perspective and draws on both specialised and common software engineering practices.
Students are required to work both independently and as part of a collaborative team in order to conduct research, analyse and define project parameters and deliver innovative solutions that expand the notion of an industry live brief.

This subject builds on the skills and knowledge students acquired from Machine Learning Principles and focuses on deep learning. It introduces students to foundational topics on neural networks, its applications to sequence modelling, computer vision, generative models and reinforcement learning. Focus will be given on learning how to model and train neural networks to implement a variety of computer vision applications. Students will 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 subject.

This subject extends students’ skills and knowledge learned in Machine Learning Principles and Applications of Artificial Intelligence. It discusses application of statistical and other machine learning algorithms to intelligently analyse written and spoken language. It begins with discussion of foundation concepts in natural language processing (NLP) and speech recognition such as language modelling, formal grammars, statistical parsing, machine translation, and dialog 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.

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