MSc(Research) - Artificial Intelligence as a main subject
The Master of Science (Research) is an internationally-recognised qualification, suitable for students who want a complete research experience, coupled with taught papers.
Artificial Intelligence is impacting on our lives, business and environment. Knowing more about this transformational technology can put you ahead on your chosen career path and put you in the driver’s seat as New Zealand positions itself as a global leader in AI.
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|Years:||1 - 1.5|
|Points:||120 - 180|
|Start Dates:||Trimester A (March) and Trimester B (July)|
|Estimated Fees* (Domestic):||$7,835 - $8,797 per year|
|Estimated Fees* (International):||$37,145 (120 points) or $56,670 (180 points) per year|
|Entry Requirements:||Postgraduate International|
|Area of Study:|
|*Tuition fees shown are indicative only and may change. There are additional fees and charges related to enrolment please see the Table of Fees and Charges for more information. You will be sent an enrolment agreement which will confirm your fees.|
- Artificial intelligence architect
- Big data engineer
- Business analyst
- Data analyst/scientist
- Database programmer
- Financial analyst
- Machine learning engineering
- Market research analyst
- Mathematical modeller
- Mathematics or computer science training
- Multimedia content creator
- Network architect
- Operations researcher
- Research programmer
- Software developer
- Systems analyst
- Usability engineer
My masters focuses on machine learning and artificial intelligence application in health and biotechnology. There is so much room to make our health systems more efficient for patients and doctors, and machine learning and AI will be at the core of this.
Read stories from other Artificial Intelligence students
Papers available within Artificial Intelligence
Prescriptions for the PGDip(AI), BSc(Hons), MSc and MSc(Research)
To complete a PGDip(AI), students must complete 120 points at 500 level including COMPX525 and at least another 75 points from papers listed for Artificial Intelligence.
Enrolment in papers towards the BSc(Hons) is only by invitation of the Head of School. To complete a BSc(Hons) in Artificial Intelligence, students must complete 120 points at 500 level, including COMPX525 and at least another 75 points from papers listed for Artificial Intelligence, of which at least 30 points must be in research (normally AIMLX591).
To complete an MSc in Artificial Intelligence, students admitted under section 2(a) of the MSc regulations must complete 180 points at 500 level including AIMLX592, COMPX525, and at least another 45 points from papers listed for Artificial Intelligence.
To complete an MSc (Research) in Artificial Intelligence, students admitted under section 2(a) of the MSc (Research) regulations must complete 180 points at 500 level consisting of AIMLX594, COMPX525, and another 45 points from papers listed for Artificial Intelligence.
|Code||Paper Title||Points||Occurrence / Location|
|AIMLX591||Artificial Intelligence Dissertation||30.0||22X (Hamilton)|
|A report on findings of a theoretical or empirical investigation.|
|AIMLX592||Artificial Intelligence Dissertation||60.0||22X (Hamilton)|
|A report on the findings of a theoretical or empirical investigation.|
|AIMLX594||Artificial Intelligence Thesis||120.0||22X (Hamilton)|
|An externally examined piece of written work that reports on the findings of supervised research.|
|COMPX521||Machine Learning Algorithms||15.0||22A (Hamilton)|
|This paper exposes students to selected machine learning algorithms and includes assignments that require the implementation of these algorithms.|
|COMPX523||Data Stream Mining||15.0||22A (Hamilton)|
|Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on and develops new incremental algorithms that process streams under strict resource limitations.|
|COMPX525||Deep Learning||15.0||22A (Hamilton)|
|This paper provides an introduction into Deep Learning, focussing on both algorithms and applications. It covers both the basics of Neural networks and current mainstream and advanced Deep Learning technology.|
|COMPX529||Engineering Self-Adaptive Systems||15.0||22A (Online)|
|Software-intensive systems need to manage themselves to fulfill dynamic requirements in a changing environment. Self-adaptation is employed in clouds/datacenters, digital twins, networks, IoT, autonomous robots, etc. Adaptation challenges include self-configuration, self-optimization, self-healing and self-protection.|
|COMPX556||Metaheuristic Algorithms||15.0||22B (Hamilton)|
|Metaheuristic are stochastic search algorithms for solving massive scale combinatorial problems where exact algorithms do not exist. This paper explores the state-of-the-art metaheuristics such as GRASP, particle swarm optimisation, and parallel metaheuristics, along with their applications in operations research, science and engin...|
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