Building a robust and trustworthy (semi-)autonomous agent requires us to build a consistent picture of the state of the world based on the data received from some perception module.
Honours and Minor Thesis projects
Please note this advert is for a Internship. It is not currently an advertisement for an honours or masters thesis project.
Please note you can ONLY apply for this internship via the internship application form.
In this project, you will build an autonomous agent in the MineRL environment for playing Minecraft or an agent for Animal-AI. Herein, you will learn how to incorporate symbolic prior knowledge for improving the performance of an agent trained by using deep reinforcement learning (RL) technique, which is the core technique to build AlphaGo.
Note: this is advertising for summer 2022 internship project (not an Honours Project)
The energy industry is evolving, and transiting to a new era with renewable energy being at the forefront. Making Australia aware of the lessons from the past and the predictions for the future is essential for us to start to understand how the country is changing for the better and what still needs to be done to ensure a more sustainable energy future for the population.
Please note this advert is for a Summer Internship as part of a collaboration between FIT, Arts and MADA. It is not an advertisement for an honours or masters thesis project at present. Please note you can ONLY apply for the internship via the Monash internship page.
(This is *not* a minor thesis or honours project, but a summer scholarship project advert only available to existing Monash taught students).
This project provides an opportunity to build on an existing funded project that focussed on document annotation using a web platform. The idea of this project is to build systems that can help humans add labels to documents more rapidly.
Android is a mobile operating system that occupies 72.11% market share globally. As the most popular mobile operating system, the android mobile app industry has been active for over a decade, generating billions of dollars in revenue for Google and thousands of mobile app developers. Several third-party Android app stores in China are estimated to generate over $8 billion in yearly revenue. Meanwhile, the number of bugs and vulnerabilities in mobile apps is growing. In 2016, 24.7% of mobile apps contained at least one high-risk security flaw.
For most engineering applications we use metals and alloys (mixture of metals) for components that need to carry significant loads. These materials have an elastic limit beyond which they start to deform irreversibly. Such irreversible deformation, called plasticity, is generally of very discrete nature and the development of such discrete strain patterns, particularly during the early stage of plasticity, is very poorly understood.
The existing deep learning-based time series classification (TSC) algorithms have some success in multivariate time series, their accuracy is not high when we apply them to brain EEG time series (65-70%). This is because there is a large variation between EEG data of different subjects, so a TSC model cannot generalise on unseen subjects well. In this research project, we investigate self-supervised contrastive learning to encode the EEG data. This way we can better model the distribution of our EEG data before classifying it into different mental statuses. See recent work here .
Feedback is crucial to learning success; yet, higher education continues to struggle with effective feedback processes. It is important to recognise that feedback as a process requires both teachers and students to take active roles and work as ‘partners’. This project seeks to enhance effective feedback processes by 1) exploring the alignment between current feedback practice with student-centred feedback principles and 2) investigating into student experience with feedback. The overall project will adopt mixed methods explained as follows: