Available Master topics: Machine Learning
Memory training exercises are known to have a positive effect on improving the memorability of humans. A memory training task can be as simple as trying to remember a password of increasing complexity or a number of varying length.
Use machine learning and video processing techniques to automatically find events in sports videos. For example, in football, some "easy" ones are goals, but others like tackles are harder.
Investigate, develop and evaluate data-driven techniques and prototypes that help software engineers build software systems that are autonomously self-healing. These are systems that can understand when they are not operating correctly and, without human intervention, make the necessary adjustments to restore themselves to normal operation.
Building a machine learning model using data plane programming language such as P4 that detect network security attacks at line rate with high accuracy
Explainable Artificial Intelligence methods (XAI) represent methods to understand and interpret machine learning (ML) methods, and have recently received a lot of attention. In this project we will also look into if XAI methods can be used to detect data outliers.
Explainable Artificial Intelligence methods (XAI) represent methods to understand and interpret machine learning (ML) methods, and have recently received a lot of attention. In this project, we will explore the potential of using XAI to improve the prediction performance of ML methods.
This project meets the demand for enhanced approaches by harnessing LLMs to elevate software engineering practices in specific research domains.
Survey experiments in studies of political and electoral behavior using profiles of potential candidates are standard practice in political science. With this type of experiments, the researchers' goal is to test which characteristics voters value most in political candidates. One of the main challenges, however, is to generate realistic profiles to be tested. One of the important components of these profiles are the faces of candidates.
A potential option is to use faces of real candidates, but this involves complex legal and ethical issues. Another option is to hire models to represent candidates, but this can be expensive and time-consuming.
The accuracy of machine learning models used in clinical decision making has a direct impact on a patient's chances of recovery. Missing data pose a challenge and generative models can assist overcome it.
Generative models represent a fascinating group of methods that can generate new samples (such as images) with similar properties to the data used to train the generative model. The models have also been used to perform generative forecasts, such as the next frames of a video or the weather for the next hours. However making these methods perform well in such cases is challenging. In this project, we will explore the potential of using simpler models to learn the temporal properties, and only use generative models to learn the spatial dependencies.
Image to image translation (I2I) represents a fascinating group of methods that translate images from an input to an output. For example, the input can be an image of a summer landscape, and the output being images of how the same landscape could look during winter. Or the input image could be a medical image of a healthy patient, and the output shows how the image would look if the patient had some disease. Naturally, we expect that it should be a variation in how the output images should look. For example, the winter images can represent little snow or much snow, or the medical output images different stages of a disease.
This research project seeks to transform cancer registry testing by harnessing the power of Large Language Models (LLMs) like ChatGPT, offering automated, generative testing methods to detect anomalies, create test cases, and enhance data quality.
This project aims to evaluate various imputation methods specifically for missing data in biological contexts. The student can also choose to examine the scalability of known methods or to develop new techniques that are scaleable.
Build an automated system based on machine learning to diagnose fault conditions in mobile communication networks.
Development of methods to improve metabolomics data handling for predicting dry eye disease.
Up for quantum-powered solutions to real-world optimization or machine learning?
Automatic classification of sport news into different categories using state of the art Natural Language Processing (NLP). The project is building upon an existing dataset of Norwegian soccer news.
How can we use mathematical optimization, uncertainty quantification, and asymptotic statistics to develop a computational framework that takes us from data and training statistical models to actually making robust, risk-averse decisions based on the predictions of these models?