What is Artificial Intelligence?
A career of the future
Embark on a rewarding career in the future-facing field of creative tech thanks to our Bachelor of Software Engineering in Artificial Intelligence. You’ll become a highly sought-after developer with a deep understanding of algorithms and techniques, plus the ability to master the fundamentals of machine learning. Developed in partnership with IBM, this qualification is the first of its kind in New Zealand. There’s never been a better time to start your studies, so apply today.
This programme is available both on campus and online.
Why Study AI in 2022?
You should study AI (Artificial Intelligence) in 2022 as it is a fast-growing industry with many employment opportunities across a range of industries. You will gain the necessary skills and knowledge required to use machines for automation. As a result, you will be able to develop cutting-edge tools and systems to improve everyday life.
Why study Artificial Intelligence (A.I.) at Media Design School?
Course Outline
What you'll cover in this course
Your first year will cover the foundational skills of software engineering and AI, including an introduction to computer graphics and practical mathematical skills. You’ll develop an understanding of basic knowledge representation, problem solving techniques and architectures used to build intelligent systems.
| Component Name | Credits | Toggle |
|---|---|---|
|
Maths 1
|
15
|
|
|
This component introduces students to foundational mathematical concepts necessary for
specialisation components 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. Level 5
MAT101
PC4133
|
||
|
Introduction to Software Engineering
|
15
|
|
|
This component introduces the information and skills needed to begin working in
software engineering. This component will cover the concepts of object-oriented programming with a particular focus on learning to use the C++ programming language. An understanding of C++ will form the basis of the necessary skills needed for developing professional and complex software packages. Level 5
ISE102
PC4133
|
||
|
Algorithms & Data Structures
|
15
|
|
|
Students learn the fundamental data structures and algorithms that are needed to solve common software engineering problems. Lecturers show examples of data structures and algorithms and use analogies to explain. Students improve their learning throughout this component by working on a number of projects. They solve common problems by designing, developing, implementing, testing, and enhancing a collection of data structures and algorithms
Level 5
ADS103
PC4133
|
||
|
Introduction to Computer Graphics
|
15
|
|
|
Students are introduced to the fundamental topics of core computer graphics, 3D graphics programming, and the rendering pipeline. By the end of the component, students create a project utilising 3D graphics concepts.
Level 6
ICG202
PC4133
|
||
|
Micro-services Architecture
|
15
|
|
|
In this component, 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.
Level 5
MSA106
PC4133
|
||
|
Probabilities and Statistics
|
15
|
|
|
This component 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 component 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.
Level 5
PST107
PC4133
|
||
|
Concepts in AI
|
15
|
|
|
The goal of this component 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.
Level 5
CAI104
PC4133
|
||
|
Introduction to Cloud Computing
|
15
|
|
|
In this component, students learn the fundamental elements of Cloud Computing. They identify the building blocks of Cloud Computing including essential characteristics, different service models and how these models differ from each other. Students also develop an understanding of resource pooling and virtualisation in the Cloud. They learn about various deployment models in cloud computing and how these deployment models differ from traditional IT deployment models
Level 5
ICC104
PC4133
|
||
|
Introduction to DevOps
|
15
|
|
|
In this component, students learn the definition, history, value, building blocks, and scope of DevOps. They also learn the process of unification and collaboration between development and operations. Students are introduced to key concepts, benefits, tools, and practices of implementing Continuous Integration, Continuous Testing, and Continuous Deployment. They also analyse the process of automation in DevOps.
Level 5
IDO107
PC4133
|
||
Delve deeper into the world of software engineering with an intro to data science and a focus on the applications of AI. You’ll extend the statistical and mathematical concepts covered in year 1, while discovering machine learning principles and exploring the vital field of human-centred design.
| Component Name | Credits | Toggle |
|---|---|---|
|
Creative Enterprise
|
15
|
|
|
This component introduces students 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. Students will learn to identify opportunities, creatively solve problems, network, communicate persuasively and work effectively in a team. In addition, this component will empower students to propose new ventures that focus on social change for good.
Level 6
CEN207
PC4133
|
||
|
Introduction to Data Science
|
15
|
|
|
This component introduces students to this rapidly growing field and equip them with some of its basic principles and tools as well as its general mindset. Students will learn concepts, techniques and tools they 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.
Level 6
IDS201
PC4133
|
||
|
Networking & Database Systems
|
15
|
|
|
This component introduces students to core concepts of Networking and Database Systems. Students learn the fundamentals of Database Management Systems and network topology including network architecture. They are introduced to relational database models and learn the fundamentals of the structured query language (SQL). Students will apply these concepts by completing multiple software engineering projects.
Level 6
NDS203
PC4133
|
||
|
Project Based Learning Studio: Technology
|
15
|
|
|
This component provides students 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, students will be challenged to find creative solutions for 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.
Level 6
PBT205
PC4133
|
||
|
Human Centred Design
|
15
|
|
|
This component helps students explore several important fields of general inquiry about significant intellectual issues related to human beings so they can view everyday problems and formulate solutions in new ways. Human Centered Design is to give students an appreciation of the factors that influence human behaviour and interactions so that they can apply specialised skills to help solve problems that affect diverse societies.
Level 6
HCD206
PC4133
|
||
|
Applications of Artificial Intelligence
|
15
|
|
|
This component builds on the skills and knowledge students 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. 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 component also covers the AI for Good movement and how AI is being used to address economic and socially relevant problems.
Level 6
AAI202
PC4133
|
||
|
Classification and Regression
|
15
|
|
|
This component introduces students 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 the students are asked to apply the learned concepts on different classification and regression problems.
Level 6
CLR204
PC4133
|
||
|
Cloud Application Development
|
15
|
|
|
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.
Level 6
CAD202
PC4133
|
||
|
Network Design
|
15
|
|
|
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. Level 6
NWD204
PC4133
|
||
Expand your technical knowledge and round out your skill set with a focus on data mining, visualisation and creative enterprise. In your final semester you’ll explore intuitive approaches to AI, with advanced tech-work integrated learning culminating in the production of an industry-standard capstone project.
| Component Name | Credits | Toggle |
|---|---|---|
|
Advanced Tech - Work Integrated Learning
|
45
|
|
|
This component 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, practising 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 component 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: Internship/volunteer Students are offered the opportunity to work within a technology company as an intern or volunteer at technology non-profit. 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 component 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 component is delivered from across specialisation perspective and draws on both specific and common software engineering practices. Students are required to work both independently or as part of a collaborative team in order to conduct research, analyse and define project parameters Level 7
ATW306
PC4133
|
||
|
Machine Learning Principles
|
15
|
|
|
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 students to the applications of machine learning, such as robotics, data mining, computer vision, bioinformatics, and natural language processing.
This component requires students to have programming skills and knowledge in probability, statistics, regression, and classification. Level 7
MLP301
PC4133
|
||
|
Natural Language Processing & Speech Recognition
|
15
|
|
|
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
Level 7
NLP303
PC4133
|
||
|
Deep Learning
|
15
|
|
|
This component 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. A 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 component. Level 7
DLE305
PC4133
|
||
|
Tools for DevOps
|
15
|
|
|
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.
Level 7
TDO301
PC4133
|
||
|
Data Mining & Visualisation
|
15
|
|
|
This component aims 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.
Level 7
DMV302
PC4133
|
||
|
Secure by Design
|
15
|
|
|
The intended aim of this component is to equip students with fundamentals of Secure by Design and enable abstraction of its underlying key principles. The course content is oriented towards 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, secure design and development best practices, introduction to encryption, and introduction to the classification of security flaws and application security.
Level 7
SBD303
PC4133
|
||
|
Scaling & Monitoring
|
15
|
|
|
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.
Level 7
SCM305
PC4133
|
||
Entry Requirements
Entry Requirements
To apply for this degree, you'll need a minimum qualification of NCEA University Entrance or equivalent, such as:
An appropriate qualification from an overseas secondary school or tertiary institution, deemed by Media Design School to be sufficient for admission into a bachelors programme.
CIE (University of Cambridge International Examination)
IB (International Baccalaureate)
Plus, you will need to have fulfilled the following credit requirements:
28 NCEA Credits* at Level 3, in a range of the following subjects: Mathematics, Physics, Computing and Technology.
*Note: Students completing NCEA Level 3 in 2020 will only require 24 credits in the above subjects. Learn more about NCEA and UE in 2020 here.
If you are enrolled at a New Zealand secondary school and have not yet completed your NCEA (CIE or equivalent) qualification, you can still apply now for admission. When your NCEA results are available in January, we will check them and contact you.
Special Entry, Discretionary Entry and Cross Credits
If you don't have university entry, you may still be able to apply for this course. More information about Special Entry, Discretionary Entry and Cross Credits here.
International Students: If you are an international student, you'll need to prove you have sufficient English language skills in order to complete this course. We'll be looking for Academic IELTS overall score of 6.0 (minimum) with no band less than 5.5, or equivalent qualification if English is not your first language.
Please note, quotas may apply to some programmes. Where demand exceeds the number of available places, applicants who meet entry requirements will be admitted on a first-come-first-served basis.
Language Requirements
Language Requirements
If English is not your first language, you may be required to provide satisfactory evidence of your proficiency in English in the form of a recent IELTS (International English Language Testing System) score , TOEFL (Test of English as a Foreign Language) score or a Pearson’s Test of English (PTE) from an accredited organisation.
BYOD Requirements
BYOD Requirements
Before you begin your study with us, you will need to have a suitable device that has the functionality to run the programmes required for your course.
Follow this link to find course-specific requirements and recommendations, along with links to more information about hardware specifications. We have prepared these recommendations to help our students equip for flexible, blended learning.
Careers & Industry
Careers & Industry
A degree in Software Engineering - Artificial Intelligence could take you down the following career paths:
- Software Engineer
- Software Developer
- Machine Learning Engineer
- AI Developer
- Business Intelligence Developer
- R&D Engineer
- Data Scientist
Software Engineers remain on New Zealand's long-term skill shortage list and the ever-growing demand for graduates means salaries remain highly competitive. We've worked closely with our industry panel to design a course that responds to industry needs, arming you with the most in-demand technical skills and the core soft skills needed to maximise your employability.
Throughout your studies you'll master a range of technical subject areas, such as computer vision, natural language processing, speech recognition, and machine learning & robotics, as well as the in-demand soft skills of ideation, design thinking, project and time management and interpersonal communication. Your first year will cover the foundational skills of software engineering and AI, including an introduction to computer graphics and practical mathematical skills.