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Honours and Minor Thesis projects

Displaying 71 - 80 of 224 honours projects.


Primary supervisor: Maria Garcia De La Banda

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.

Primary supervisor: Lizhen Qu

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.

Primary supervisor: Reuben Kirkham

(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. 

Primary supervisor: Xiao Chen

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.

Primary supervisor: Mahsa Salehi

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 [1].

Primary supervisor: David Dowe

Turning Point’s National Ambulance Surveillance System is a surveillance database comprising enriched ambulance clinical data relating to alcohol and other substance use, suicidal and self-injurious thoughts and behaviours, and mental health-related harms in the Australian population. These data are used to inform policy and intervention design and are the subject of ever-increasing demand from academic professionals and units, government departments, and non-government organisations.

 

Primary supervisor: David Dowe

Develop, implement, and test deep learning techniques for automatic classification of epileptic seizures using video data of seizures

Primary supervisor: Levin Kuhlmann

This project takes a different approach to RL, inspired by evidence that Hippocampus replays to the frontal cortex directly. It is likely used for model building, as opposed to the mainstream view in cognitive science and ML - where 'experience replay' ultimately improves policy. The predicted benefits are sample efficiency, better ability to generalize to new tasks and an ability to learn new tasks without forgetting old ones.

Primary supervisor: Levin Kuhlmann

The brains of all bilaterally symmetric animals on Earth are divided into left and right hemispheres. The anatomy and functionality of the hemispheres have a large degree of overlap, but they specialize to possess different attributes. This principle is poorly understood and has not been exploited in AI/ML. The right hemisphere is more dominant for novelty, and the left for routine. Activity slowly moves to the left hemisphere as a task is perfected. In this project, we apply that principle to continual RL, where new tasks are introduced over time…

Primary supervisor: Levin Kuhlmann

The hippocampus is critical for episodic memory, a key component of intelligence, and a sense of self. There are a number of computational models, but none of them consider the fact that the hippocampus is, like the rest of the brain, divided into Left and Right hemispheres. Division into Left and Right is poorly understood, but undoubtedly critical, as it is a remarkably conserved feature of all bilaterally symmetric animals on Earth.