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The increasing prevalence of mental health issues worldwide has amplified the significance of computational psychotherapy, which includes creating computational tools for the mental healthcare and tools to support existing mental health professionals. This work presents a computational psychotherapy system that predicts and forecasts mental health issues in users, and utilizes …

In optimization, it is well known that algorithm performance is dependent on the problem being solved. As a consequence of this, achieving good optimization results requires correctly matching an optimization problem to a specific optimization algorithm that performs well on that problem. For this to be possible, knowledge of both …

We are speeding full throttle towards a climate catastrophe and we have only until 2030 to take action to avoid the most devastating and unimaginable consequences. The lack of political and corporate will to change the status quo of “business as usual” is causing mass protests and civil disobedience globally. …

With the resurgence of neural network-based learning in the last decade, machine learning methods are becoming critical components of many real-life intelligent systems. However, while being able to learn effectively and at scale, such systems are often non-interpretable and unable to exploit existing symbolic background knowledge. The paradigm that offers …

Due to the nature of the wireless transmission medium, wireless communications are characterised by notably larger losses of data packets than wired communications. The quality of wireless links is highly dependent on channel variations, interference and even transceiver imperfections. Such link uncertainty instigated the development of numerous techniques that can …

As urbanisation continues to be a trend, in which centralisation of the population into cities is still growing, the importance of intelligent solutions in mobility is in high demand. Likewise, with the integration of renewable energies into all levels of the electrical grid system and the increasing amount of heavy …

Most machine learning, data mining and statistical methods rely on the assumption that the analyzed data are independent and identically distributed (i.i.d.). More specifically, the individual examples included in the training data are assumed to be drawn independently from each other from the same probability distribution. However, cases where this …

In the thesis, we address the task of polynomial regression, i.e., inducing regression models based on polynomial equations, from data. We aim at improving and extending the existing approaches to learning polynomial regression models in several directions. First, we improve the existing methods for addressing the issue of over-fitting and …

In this thesis we address the problem of learning various types of decision trees from timechanging data streams. In particular, we study online machine learning algorithms for learning regression trees, linear model trees, option trees for regression, multi-target model trees, and ensembles of model trees from data streams. These are …

Can a model constructed by machine learning or data mining programs be trusted? For example, it is known that a decision tree model can contain less-credible parts caused by pathologies in induction algorithms, noise and missing values in data, or simply because of the complexity of a domain. Such models …