Artificial Intelligence
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.
The growth of available data (“Big Data”) across companies, industry, government agencies and departments, iwi and community organisations has meant more and more of them are adopting AI into their mainstream activities. This has led to an increased demand for graduates with advanced skills in AI.
Studying AI at the University of Waikato will give students advanced skills and techniques to better leverage AI for the benefit of our communities.
The University of Waikato is well known worldwide for its computer science research and for development of open source software such as WEKA, MOA and ADAMS, with more than 10 million downloads. The University has recently invested in New Zealand’s most powerful computer for AI, it is also the home of Te Ipu o te Mahara, The Artificial Intelligence Institute.
Students have the opportunity to work alongside some of the world's leading AI researchers, with particular expertise in machine learning, deep learning, real-time analytics and open source software.
Key information
| Study Location: | Hamilton |
|---|---|
| Area of Study: |
Study Artificial Intelligence in these qualifications
Study Artificial Intelligence as a specialisation of
Career opportunities
- 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
Papers
Available Artificial Intelligence papers
300 Level
| Code | Paper Title | Points | Occurrence / Location |
|---|---|---|---|
| COMPX301 | Design and Analysis of Algorithms | 15.0 | 23A (Hamilton) |
| This course gives a survey of advanced algorithms and analysis of their performance, along with heuristic methods that include basic Artificial Intelligence techniques. | |||
| COMPX307 | Principles of Programming Languages | 15.0 | 23B (Hamilton) |
| The design, implementation and use of programming languages, in particular the use of functional languages to implement imperative languages will be studied. Assignments will involve challenging programming problems. | |||
| COMPX310 | Machine Learning | 15.0 | 23B (Hamilton) & 23B (Tauranga) |
| This paper introduces Machine Learning (ML) which is the science of making predictions. ML algorithms strive to be fast and highly accurate, while processing large datasets. This paper will use standard Python-based ML toolkits to teach the fundamentals of ML. | |||
| COMPX323 | Advanced Database Concepts | 15.0 | 23A (Hamilton) |
| This paper provides an introduction to the advanced features of database management systems. Students will learn to use and manipulate advanced features, and to understand and explore the technical background of large database management systems. They will have hand-on practice in using these features to create, query and maintain ... | |||
| COMPX361 | Logic and Computation | 15.0 | 23B (Hamilton) & 23B (Tauranga) |
| The syllabus includes: further development of predicate logic with application to program verification; mathematical induction including structural induction; finite state automata and regular languages; Kleene's Theorem; Turing machines, the Church-Turing thesis, universal Turing machines and the Halting problem; formal grammars a... | |||
| COMPX367 | Computational Mathematics | 15.0 | 23B (Hamilton) |
| Introduces numerical methods for solving various mathematical problems. | |||
| DATAX322 | Probability and Stochastic Processes | 15.0 | 23A (Hamilton) |
| This paper introduces students to probability theory and stochastic processes. It covers formally the theoretical foundations of probability, random variables, statistics, stochastic processes and Markov chains. | |||
500 Level
| Code | Paper Title | Points | Occurrence / Location |
|---|---|---|---|
| AIMLX591 | Artificial Intelligence Dissertation | 30.0 | 23X (Hamilton) |
| A report on findings of a theoretical or empirical investigation. | |||
| AIMLX592 | Artificial Intelligence Dissertation | 60.0 | 23X (Hamilton) |
| A report on the findings of a theoretical or empirical investigation. | |||
| AIMLX594 | Artificial Intelligence Thesis | 120.0 | 23X (Hamilton) |
| An externally examined piece of written work that reports on the findings of supervised research. | |||
| COMPX521 | Machine Learning Algorithms | 15.0 | 23B (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 | 23A (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 | 23A (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. | |||
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
Scholarships and prizes
New to Waikato? The International Excellence Scholarship is worth up to $10,000.
Visit our Scholarship Finder for information about possible scholarships
Contacts
School of Computing and Mathematical Sciences
Phone: 0800 924 528 or +64 7 838 4625
Email: [email protected]
Website: cms.waikato.ac.nz
Facebook: https://www.facebook.com/WUcms