AI-related papers (courses) at AUT

AUT offers a variety of papers related to AI. These can be taken as part of a degree, or as a Certificate of Proficiency.

Undergraduate papers

These papers can be studied as part of:

Artificial Intelligence

Semester 1/2018
The aim of this paper is to understand the nature of intelligent systems and how such a system may be implemented.

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Data Mining and Knowledge Engineering

Semester 2/2018
Introduces students to the exciting world of Data Mining. Organisations have, over time, accumulated vast amounts of valuable data that, when exploited appropriately, will give them a significant competitive advantage over their rivals who merely "crunch" data. Data Mining is an area that has come of age - well proven techniques and tools are widely available. Covers popular mining techniques as well as providing specific hands-on experience using a publicly available tool.

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Embedded Digital Systems

Semester 2/2018
To create a complete embedded system from the initial design, functional and technical descriptions to the final electronic circuit, structure diagram and C code.

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Intelligent Agents and Environments

Semester 1/2018
This paper explores the development of intelligent agents that integrate software and hardware technologies. The paper introduces a range of artificial intelligence techniques for developing intelligent agents and environments, techniques suitable for rationalising conflicting input data and improving system responses as well as appropriate sensor technologies that can be used to gather data from the environment.

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Nature Inspired Computing

Semester 1/2018
Provides an overview of the fundamentals of nature inspired computing (NIC), particularly in the fields of evolutionary algorithms, swarm intelligence, artificial life, DNA computing and quantum computing. Real world applications in optimisation, simulation and modeling will be introduced in workshops and recent advances presented and analysed.

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Text and Vision Intelligence

Semester 2/2018
This paper covers fundamental and advanced aspects of language (especially text) and vision from an artificial intelligence perspective. The primary focus is on practical algorithms, tools and systems of text, vision intelligence, as well as their performance evaluation.

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Postgraduate papers in computer sciences

These papers can be studied as part of:

Artificial Intelligence

Semester 2/2018
Critical appraisal of the key concepts underpinning artificial intelligence programs. Analysis and critique of different computational approaches in artificial intelligence (AI).

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Bioinformatics

Semester 2/2018
Critically analyses computational, mathematical and information processing methods for biological data collection, storage, processing and knowledge discovery, with applications to support decision making in science, engineering, medicine, health, agriculture, horticulture, and bio-protection.

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Data Mining and Machine Learning

Semester 1/2018
Studies and evaluates data mining techniques such as Decision Tree classifiers, Bayesian classifiers, Apriori techniques for discovering associations between features, clustering algorithms, and neural network technology. Critically analyses the link between traditional statistical analysis and data mining.

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Intelligent Surveillance

Semester 2/2018
Emphasises practical skills and enhances students' conceptual and analytical skills in creative thinking and problem solving. Examples of successful surveillance systems and the relevant algorithms and tools will be also introduced. On completion of the paper, students will have sufficient expertise to allow them to consider undertaking a research thesis in the area.

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Nature Inspired Computing

Semester 2/2018
Computing approaches inspired by nature, constructed using biological principles and modeled on natural processes allowing students to use, critically appraise and analyse the key concepts underpinning Nature Inspired Computing (NIC) techniques as applied to optimisation and search problems.

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Neuroinformatics

Semester 1/2018
Introduces contemporary developments in Neuroinformatics (NI) and the techniques available to work with the vast amount of NI data. Students will apply computational modeling techniques to NI data and critique the process and results. The first interdisciplinary paper on the subject offered in Australasia, this paper will suit students and researchers from various subject areas. Students choose their assignment topics for individual or group work.

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Text Mining

Semester 1/2018
Explores the issues associated with processing of knowledge represented by natural languages using a computer. Discourse will be analyzed for text structure, segmentation, cohesion/coherence and reference resolution. Text processing tasks such as document summarisation, inter-language translations, and information extraction will be examined. Also, the use of text mining for a variety of applications will be examined as case studies.

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Ubiquitous Computing

Semester 2/2018
Critically analyses the convergence of technologies enabling the delivery of rich media and useful applications in a variety of formats and to a large range of devices, investigates platform and location independent access, and context-aware systems, appraises issues related to ubiquitous applications development, wireless networking infrastructure, emerging W3C standards, emerging technologies, mobile agents and auto identification and analysis.

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Postgraduate papers in engineering

These papers can be studied as part of:

Computer Vision

Semester 2/2018
Provides the underlying notation, methods, and algorithms for processing and analysis of digital images and video, and how to apply these technologies in selected real-world scenarios. Subjects are presented in a concise mathematical and algorithmic form, and tutorials and assignments provide support for experimenting with the taught concepts. Considered scenarios are computational photography, stereo vision, motion analysis, image segmentation, 3D shape recon-struction, and object detection and tracking.

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Digital Signal Processing

Semester 1/2018
This course introduces the fundamentals of DSP techniques and their application in engineering, focusing on discrete-time signals and linear systems in both Time and Frequency domains, Fourier analysis and Z-transform, digital filter design and implementation, echo generation and speech processing, convolution and correlation. Matlab will be used as a simulation tool for the DSP algorithms and TMS320C67xx will be used to implement the algorithms. The architecture and features of some DSP processors will be covered. Digital image processing and image compression techniques and algorithms will be discussed.

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Digital Signal Procession Applications

Semester 2/2018
Advanced topics in the application of digital signal processing in: speech, image processing, radar, pattern recognition, adaptive filtering. Computational techniques and tools of signal processing; limitations of various linear and non-linear systems; software implementations applied to the analysis of real signals.

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Embedded Software Engineering

Semester 2/2018
Advanced techniques for the design, development and implementation of embedded systems including: implementing an advanced operating system on an embedded computer, development of high-level hardware-orientated applications using an appropriate language, client-server embedded systems including embedded web server development, system modeling using UML, design patterns for embedded systems, and software engineering for embedded systems.

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Intelligent Systems

Semester 2/2018
Explores computational intelligence based methodologies that handle incomplete knowledge and ill defined dynamics including: fuzzy knowledge based systems and their application in knowledge representation; neural networks and their application in machine learning and adaptive tuning; genetic algorithms and their application in random searching and optimization; uncertainty management through Bayesian reasoning.

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Intelligent Systems Engineering

Semester 2/2018
The course deals with the computational intelligence based methodologies that handle incomplete knowledge and ill defined dynamics. These include (1) fuzzy knowledge based system and their application in knowledge representation, (2) neural network and their application in machine learning and adaptive tuning, (3) genetic algorithm and their application in random searching and optimization, and (4) uncertainty management through Bayesian reasoning. The paper will also cover the neuron-fuzzy architectures and evolutionary computing. Applications and case studies within the area of industrial processes and control will also be included.

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Robotics and Automation

Semester 2/2018
Introduces fundamentals of robotics with a focus on robotic manipulators found in industrial automation. It will cover basic concepts of robotics, space transformations, forward and inverse kinematics, velocity and force analysis with Jacobians, robot dynamics and control, trajectory planning, robot programming and integration of robots with other modules for industrial automation.

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PhD studies

With a qualifying postgraduate degree such as a bachelor's (honours) or master's, you can enrol in AUT's PhD programme. For this you typically choose a topic or project that interests you, contact potential supervisors, and then join a research institute. It usually takes three to four years of full-time study during which you produce a substantial amount of research that solves or explores a problem in a new way.

Doctor of Philosophy (PhD) information

Contact us

Anne Abbott
Email: anne.abbott@aut.ac.nz

Co-founders

The co-founders of the AUT AI Initiative are: