Artificial Intelligence Subject regulations
Artificial intelligence (AI) is the combination of data, algorithms and computing power that can perform tasks that would require intelligence if they were done by a human. It is impacting our lives, businesses and environment. As a subject AI will give students advanced skills and techniques to address the problems at hand.
Other qualifications
Prescriptions for:
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GradCert(AI) and GradDip(AI) A Graduate Certificate and [Graduate Diploma](https://calendar.waikato.ac.nz/regulations/diplomas/graddip are available to graduates who have not included Artificial Intelligence at an advanced level in their first degree.
For further details, contact the Division of Health, Engineering, Computing and Science Office.
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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.
Artificial Intelligence papers
| Code and paper title | Points | |
|---|---|---|
COMPX301
Design and Analysis of Algorithms
This course gives a survey of advanced algorithms and analysis of their performance, along with heuristic methods that include basic Artificial... |
15 | 24A (Hamilton) |
|
COMPX307
Principles of Programming Languages
The design, implementation and use of programming languages, in particular the use of functional languages to implement imperative languages will be... |
15 | 24B (Hamilton) |
|
COMPX310
Machine Learning
This paper introduces Machine Learning (ML) which is the science of making predictions. ML algorithms strive to be fast and highly accurate, while... |
15 | 24B (Hamilton) & 24B (Tauranga) |
|
COMPX323
Advanced Database Concepts
This paper provides an introduction to the advanced features of database management systems. Students will learn to use and manipulate advanced... |
15 | 24A (Hamilton) |
|
COMPX361
Logic and Computation
The syllabus includes: further development of predicate logic with application to program verification; mathematical induction including structural... |
15 | 24B (Hamilton) & 24B (Tauranga) |
|
DATAX322
Probability and Stochastic Processes
This paper introduces students to probability theory and stochastic processes. It covers formally the theoretical foundations of probability, random... |
15 | 24A (Hamilton) |
| Code and paper title | Points | |
|---|---|---|
|
AIMLX591
Artificial Intelligence Dissertation
A report on findings of a theoretical or empirical investigation. |
30 | 24X (Hamilton) |
|
AIMLX592
Artificial Intelligence Dissertation
A report on the findings of a theoretical or empirical investigation. |
60 | 24X (Hamilton) |
|
AIMLX594
Artificial Intelligence Thesis
An externally examined piece of written work that reports on the findings of supervised research. |
120 | 24X (Hamilton) |
|
COMPX521
Machine Learning Algorithms
This paper exposes students to selected machine learning algorithms and includes assignments that require the implementation of these algorithms. |
15 | 24B (Hamilton) |
|
COMPX523
Data Stream Mining
Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on and develops new incremental... |
15 | 24A (Hamilton) |
|
COMPX525
Deep Learning
This paper provides an introduction into Deep Learning, focussing on both algorithms and applications. It covers both the basics of Neural networks... |
15 | 24A (Hamilton) |
|
COMPX546
Graph Theory
An introduction to graph theory and combinatorics, including network optimisation algorithms. |
15 | 24B (Hamilton) |
|
COMPX555
Bioinformatics
An introduction to bioinformatics, open to students majoring in computer science or biology. It includes an overview of molecular biology, genomics... |
15 | No occurrences |
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COMPX301 Design and Analysis of Algorithms
This course gives a survey of advanced algorithms and analysis of their performance, along with heuristic methods that include basic Artificial...
Points 15The design, implementation and use of programming languages, in particular the use of functional languages to implement imperative languages will be...
Points 15This paper introduces Machine Learning (ML) which is the science of making predictions. ML algorithms strive to be fast and highly accurate, while...
Points 15This paper provides an introduction to the advanced features of database management systems. Students will learn to use and manipulate advanced...
Points 15The syllabus includes: further development of predicate logic with application to program verification; mathematical induction including structural...
Points 15This paper introduces students to probability theory and stochastic processes. It covers formally the theoretical foundations of probability, random...
Points 15 -
AIMLX591 Artificial Intelligence Dissertation
A report on findings of a theoretical or empirical investigation.
Points 30A report on the findings of a theoretical or empirical investigation.
Points 60An externally examined piece of written work that reports on the findings of supervised research.
Points 120This paper exposes students to selected machine learning algorithms and includes assignments that require the implementation of these algorithms.
Points 15Data streams are everywhere, from F1 racing over electricity networks to news feeds. Data stream mining relies on and develops new incremental...
Points 15This paper provides an introduction into Deep Learning, focussing on both algorithms and applications. It covers both the basics of Neural networks...
Points 15An introduction to graph theory and combinatorics, including network optimisation algorithms.
Points 15An introduction to bioinformatics, open to students majoring in computer science or biology. It includes an overview of molecular biology, genomics...
Points 15
2024 Catalogue of Papers information current as of 21 Nov 2024 01:00am