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Found 7 matching student topics

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Human-in-the-loop techniques to debug machine learning models

Machine learning models are being deployed in critical domains such as healthcare, education and fintech. The current approach to deploying machine learning models is based on considering a data-centric approach where the models are evaluated using performance measures on a test set. However, the high performance of the model on test data is not indicative of its reliability,An important aspect of reliability is in the understanding of what exactly a machine learning model encodes, and to verify if it learns …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Information Systems
Research centre(s)

Machine learning for wildlife monitoring

This project will investigate methods to monitor wildlife using machine learning applied to aerial imagery.While it's highly desirable to use drones and aerial footage to monitor wildlife, there are substantial challenges created by the nature of the data and target wildlife.This, combined with the vast nature of any collected aerial data, makes manual analysis difficult. This challenge motivates the development of machine learning methods to automatically process data and perform tasks, such as:detecting target animalscounting herd animalsclassifying land useassessing environment …

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Data Science

Gesture-based control of underwater helper-bots

Underwater robotic systems have been in use for several decades. In recent years, various groups have been adding manipulators and other payloads to increase their utility. However, their role has primarily been monitoring and mapping the oceans without human interaction.The next frontier is to have human divers and robotic system collaborate safely and productively in the same space to jointly complete complex tasks. This will involve the robotic system directly understanding and interacting with the diver in a non-verbal manner. …

Study level
PhD, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)
Centre for Robotics

Machine learning in a very different future

Machine learning aims to make predictions about novel data based on associations and relationships from past data. This approach rests on the assumption that the past is representative of the future.We're exploring how statistical machine learning might be used knowing that aspects of the future are likely to be fundamentally different to the past that generated the historical training data.Our aim is to is to look for fruitful ways to couple simulation and inference so that we can take advantage …

Study level
Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Computer Science
Research centre(s)
Centre for Data Science

Deep learning for robotics in open-world conditions

To fully integrate deep learning into robotics, it's important that deep learning systems can reliably estimate the uncertainty in their predictions. This allows robots to treat a deep neural network like any other sensor and use the established Bayesian techniques to fuse the network’s predictions with prior knowledge or other sensor measurements or to accumulate information over time.Deep learning systems typically return scores from their softmax layers that are proportional to the system’s confidence. They are not calibrated probabilities and …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Electrical Engineering and Robotics
Research centre(s)

Equation learning for partial differential equation models of stochastic random walk models

Random walk models are often used to represent the motion of biological cells. These models are convenient because they allow us to capture randomness and variability. However, these approaches can be computationally demanding for large populations.One way to overcome the computational limitation of using random walk models is to take a continuum limit description, which can efficiently provide insight into the underlying transport phenomena.While many continuum limit descriptions for homogeneous random walk models are available, continuum limit descriptions for heterogeneous …

Study level
PhD, Master of Philosophy, Honours, Vacation research experience scheme
Faculty
Science and Engineering Faculty
School
School of Mathematical Sciences
Research centre(s)
Centre for Data Science

Making machine learning understandable for human decision support in credit risk assessment

Credit risk assessment is an important aspect in personal loan applications because of an increase in loan applications and a decline in risk appetite of lending institutions. Traditionally, predictions of credit risk (i.e. the weighted probability of loan default) are based on individual socio-economic information, loan application information and dynamic transaction data. Although machine learning models have been applied to the evaluation of credit risk, there is a gap to their use for decision making because many of these methods …

Study level
Master of Philosophy
Faculty
QUT Business School
School
School of Accountancy
Research centre(s)

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