To main content

New Methods in Machine Learning

Photo: Shutterstock
Photo: Shutterstock

1. Expainable AI using Shapley-based methods

Shapley-based methods in order to explain black box models has become a popular application within explainable AI. This includes SHAP values and SAGE values. Interactions is a specifically interesting effect, where Shapley-based methods may be used, however with demanding computational resources. In addition, using Shapley values for high-dimensional data, and where features are correlated makes it even more complicated. This project concerns how to deal with these issues, for instance based on tree-ensemble models or other black-box models.
Contact: Sølve Eidnes/Pål Johnsen

2. Port-Hamiltonian neural networks

This is a relatively new concept of semi-constrained machine learning, that has so far only been applied to toy models. We are stress-testing the framework for industrial application in safety critical systems, so we need to understand how it is affected by data quality such as noise or missing measurements, as well as how to scale up from toy models to complicated systems.
Contact: Sølve Eidnes/Alexander Stasik

3. Reservoir computing for industrial applications

Reservoir computing is a machine learning paradigm for dynamical systems which relies on recurrent models like RNNs, SNN or quantum computing systems. In contrast to classical ML, here the model is not trained, only it’s output weights, resulting in lower requirements for training data and noise resistance. While those systems have been studied in theory, the aim of this project is to apply it to model and control real-world systems.
Contact: Alexander Stasik

4. Optimal sampling of expensive measurements

Modelling and control of dynamical systems relies on input data. Often, taking measurements is expensive. The goal of this project is to study how to minimise observations while still controlling the system in a safe way.
Contact: Alexander Stasik

5. Lie Detection for a General Neural Network Classifier

Neural Network Classifiers are used in a variety of fields, from relevance indicator to the automotive industry. However, even when a classifier selects a class with very high confidence, the classification can still be wrong. In this project we will research and develop techniques to correctly detect wrong high-confidence classifications.
Contact: Filippo Remonato

6. Data-Driven Residual Correction of a First-Principles Model

First-principle models are based on known physical, chemical, or mathematical theory, and deliver highly accurate predictions when the underlying theoretical assumptions are met. This is, however, rarely the case in the real world. Machine Learning and other data-driven methods can be used in support of a first-principle model to correctly adapt for the uncertainty contained in the real-world data. This project concerns the development of a data-driven correction module for a first-principle model of chemical reactions.
Contact: Filippo Remonato

7. Time series modelling for industrial processes

Since it’s breakthrough in the last decade, AI has been most prominent and promising on problems that are digital in nature, like image analysis, language models and targeted advertising. Industry applications can include physical, chemical and biological systems that bring along several complicating issues. Most industrial systems at present are often highly complex and of a nature that makes it difficult to get data of sufficient quality and quantity to use purely data-driven methods. Furthermore, one often must consider safety-critical systems, which puts extra requirement on the precision and trustworthiness of the models used. This project concerns the development and testing of techniques for dealing with industrial time series data, with the goal of making precise and reliable forecasting models.
Contact: Sølve Eidnes

8. Metric, features, and modelling testing in a commercial real estate scenario

The success of a commercial concept (bar, restaurant, shop, hairdresser, …) is the result of a multitude of factors: The offerings, of course, but also the area, the weather, the proximity to public transport, the surrounding competition, and even some surprising synergies between apparently independent businesses, to name a few. This is what intuition and experience tells us, but how can we define it in concrete ways? What metric can be used to define “success” of a commercial concept? What features relevantly influence the success, and which have little to no effect? How can we best model this relationship? This project will explore this extremely variegated and multi-faceted problem, and can be tailored according to the student’s interests to focus in any of these dimensions of what makes a commercial concept work well, or fail.
Contact: Filippo Remonato

These projects can be adapted to students from several departments such as Computer Science, Mathematical Sciences or Engineering Cybernetics.

Contact & Questions