MSc - Artificial Intelligence as a main subject
The Master of Science is valued for its flexibility and excellence, and can be tailored to suit your needs. You can select your papers from a range of subjects and the mix of research and taught papers are customised to suit your interests and goals.
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):||$8,050 - $9,039 per year|
|Estimated Fees* (International):||$40,860 (120 pts)
$61,290 (180 pts)
|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 PGCert(AI), PGDip(AI), BSc(Hons), MSc and MSc(Research)
To complete a PGCert(AI), students must complete 60 points at 500 level consisting of 60 points from papers listed for Artificial Intelligence.
To complete a PGDip(AI), students must complete 120 points at 500 level including at least 90 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 at least 90 points from the 500-level 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, and at least another 60 points from the 500-level 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, and 60 points from the 500-level papers listed for Artificial Intelligence.
|Code||Paper Title||Points||Occurrence / Location|
|AIMLX591||Artificial Intelligence Dissertation||30.0||24X (Hamilton)|
|A report on findings of a theoretical or empirical investigation.|
|AIMLX592||Artificial Intelligence Dissertation||60.0||24X (Hamilton)|
|A report on the findings of a theoretical or empirical investigation.|
|AIMLX594||Artificial Intelligence Thesis||120.0||24X (Hamilton)|
|An externally examined piece of written work that reports on the findings of supervised research.|
|COMPX521||Machine Learning Algorithms||15.0||24B (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||24A (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||24A (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.|
|COMPX546||Graph Theory||15.0||24B (Hamilton)|
|An introduction to graph theory and combinatorics, including network optimisation algorithms.|
|An introduction to bioinformatics, open to students majoring in computer science or biology. It includes an overview of molecular biology, genomics, script language programming, algorithms for biological data, an introduction to machine learning and data mining, and relevant statistical methods.|
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