Available Master topics: Machine Learning

Memorability concept has been mostly investigated on the images using Machine Learning [1]. However, there is no measure available in the literature that can assess the difficulty of remembering a given password (word) or number.

Computer assisted semen analysis (CASA) systems has improved since the first were introduced to the marked, but they are still not accurate and need improvement. Automatic classification of sperm morphology would be of significant assistance in semen analysis.

What we want to find out is whether machine learning can be trained to create an appropriate CAD model from only the Lidar point cloud, or a Lidar point cloud with some images, if it has been trained with CAD reconstructions that have been created by humans using all the methods mentioned above.

Cardiovascular diseases are burdening the healthcare systems and the costs are expected to rise in the years to come. Acute stroke alone is estimated to cost the European countries an overwhelming 40 billion annually.


The aim of the project is to obtain a deeper understanding of GANs and develop new models and estimation techniques with improved properties.

The current state-of the-art of machine learning adopts the use of batch learning where the data is available offline, mostly divided into test and training sets.

Machine learning is a popular topic in data science, and there exists many frameworks that can be used for training and inference of these neural networks. However, many of these frameworks are still only optimised for one machine and only the CPU architecture.

The massive increase in the incidence of diabetes is now a major global healthcare challenge, and the treatment of diabetes is one of the most complicated therapies to manage, because of the difficulty in predicting blood glucose (BG) levels of diabetic patients.

The Nvidia Denver 2 CPU architecture can be found on Nvidia's Tegra X2 System-on-Chips. And it is today used in platforms such at the Drive PX2 and Jetson TX2. We want to understand the architecture of the processor and how it compares to other state of the art ARM and x86 CPUs.

As Bushido, or "the way of the samurai", is providing the guiding principles and philosophy of the samurai - in this master project, we looking for students that are interested in the fundamental part of systems and architectures that can address the challenges

Colon cancer is the third most common cause of cancer mortality for both men and women, and it is a condition where early detection is of clear value for the ultimate survival.

In this project the goal is to explore existing state of the art methods from machine learning and statistics such as Recurrent neural networks, ARMA and Long short term memory networks.

In this project, a simple environment for the emergence of general intelligence features will be investigated, where agents can evolve through environmental rewards, and learn throughout their lifetime. The chosen control system for agents is spiking neural networks.

Vast amounts of data is being collected to assess the reliability and performance of mobile broadband network and needs to be processed in order to understand the potential causes of failures [1, 2]. We started collecting this data in 2013 and it amounts to over 13 TB in volume.

Performance of many algorithms in machine learning crucially depends on one or more tuning parameters. At the same time, the issue of parameter selection and tuning still remains a big challenge for most real-life applications.

Myocardial ischemia due to coronary artery occlusion promotes cardiac tissue remodeling that increases patient risk to lethal arrhythmias and sudden cardiac death (SCD). However, determining individual patient risk to SCD remains difficult and often involves invasive techniques.