Master in Artificial Intelligence / Μεταπτυχιακό στην Τεχνητή Νοημοσύνη

Recent advancements in artificial intelligence have been significantly inspired by the field of educational data mining, or EDM. According to the needs of the students and instructors, new potential and Possibilities for the advancement of technology-enhanced learning systems have been created via a range of research and put into practice. The use of modern techniques and application strategies by the EDM is crucial in improving the learning environment. The EDM is essential in building the educational setting for scholars along with ameliorating the working conditions for instructors and giving them appropriate information regarding the necessities of the students by looking at both the educational environment and machine learning approaches. In order to reach that goal, the main focus of this thesis will be on the socioeconomic elements that may influence how various institutions, communities, and social backgrounds function at various educational levels. To evaluate student and teacher data and anticipate the critical factors that affect educational effectiveness, a variety of machine learning techniques are applied. Specialized dimensionality reduction techniques are utilized to display the data from diverse locations. Then, regions and countries are categorized based on their common educational qualities using K-Means and Spectral clustering. Two datasets have been used for this analysis. Clustering Results Based on the provided information from the first dataset, a clustering analysis was conducted across various characteristics related to lower education levels. Cluster 1 consists of countries with high working time, teaching time, administrative tasks, additional duties, course planning, amount of lessons, volume of marking, negative impact on mental health, stress, physical health, salary satisfaction, and preparedness for students with disabilities. These countries include the United Kingdom, Sweden, Iceland, Norway, the Netherlands, Croatia, the Czech Republic, and Hungary. Cluster 2 comprises countries with low working time, teaching time, administrative tasks, additional duties, course planning, amount of lessons, volume of marking, negative impact on mental health, stress, physical health, salary satisfaction, and preparedness for students with disabilities. Italy, Cyprus, Romania, Finland, and Montenegro are part of this cluster. Cluster 3 includes the remaining countries, Belgium, Bulgaria, Spain, Czech Republic, Estonia, Croatia, Austria, North Macedonia and Slovenia, which are considered to have medium characteristics across the analyzed dimensions. The second dataset has information for both lower and upper education level. Based on the information provided for the lower education level dataset, here is a summary of the clusters created: Students: Cluster 1 (High Performance): This cluster includes countries like Spain, Belgium, Sweden, Italy, Hungary, and Ireland. These countries exhibit high student performance in terms of academic achievements, participation rates, and overall educational outcomes. Cluster 2 (Moderate Performance): This cluster comprises the United Kingdom and the Netherlands. These countries demonstrate moderate student performance, with relatively average academic achievements and participation rates compared to other countries in the dataset. Cluster 3 (Mixed Performance): This cluster consists of the remaining countries. They exhibit a mix of student performance levels, with some countries showing above-average performance while others have lower performance in terms of academic achievements and participation rates. Teachers: Cluster 1 (Experienced and Well-Qualified): This cluster includes countries such as Latvia, Lithuania, Malta, and Finland. These countries have highly experienced and well-qualified teachers who undergo continuous professional development, resulting in high-quality teaching and effective classroom practices. Cluster 2 (Diverse and Competent): This cluster comprises countries like Iceland, Germany, the United Kingdom, and the Netherlands. These countries have a diverse teacher workforce with a range of competencies and expertise. They prioritize teacher collaboration, innovation, and continuous improvement in their education systems. Cluster 3 (Varied Teacher Profiles): This cluster encompasses the remaining countries. They exhibit a mix of different teacher profiles, including varying levels of experience, qualifications, and professional development opportunities. The characteristics and quality of their teacher workforce vary across these countries.

Fast and Accurate SED modelling using Machine Learning is a supervised learning project aiming at exploring efficient representations of spectral energy distribution (SED) fitting codes by leveraging the immense capabilities of machine learning algorithms that have emerged during the last decade. The focus will be on the supervised learning process of two alternative (surrogate) models, a neural network (ANN) and an ensemble regressor (HGB), based on a sufficiently large dataset of simulated galaxy spectra, generated with the CYGNUS (CYprus models for Galaxies and their NUclear Spectra) models. The project is structured into the following chapters: Chapter 1 provides a background of the problem surrounding the effective retrieval of the fundamental physical properties of galaxies by means of studying and modelling the vast and complex cosmological and astrophysical data selected by an ever-growing number of sources. Chapter 2 offers an introduction into the principles of the proposed machine learning models (ANN, HGB), their architecture, and their fundamental functions. The objective is to familiarize the reader with the terminology and the data processing methods referred to at a later stage of the project. Chapter 3 gives a description of surrogate models, along with the justification of the need to employ such models in the case of non-linear models with intractable parameter distributions. As a matter of fact, the project follows the supervised learning of selected SED surrogate models to be eventually used in the inverse process of fast and accurate retrieval of physical parameters. Chapter 4 is dedicated to the specifications and the implementation process of the end-to-end MARGE (Machine learning Algorithm for Radiative transfer of Generated Exoplanets) package. A distinctive part of MARGE is the deep learning functionality (neural network) used to train the SED surrogate model. Chapter 5 outlines the complete training pipeline of this project, starting from the justification of the CYGNUS model. The training data for the proposed machine learning models are made up of one million CYGNUS model simulations of the combined form (input model parameters – output spectra). The most important hyperparameters of the predictive models (ANN, HGB) are optimized on a subset of the data and optimal models are trained on the entire dataset. Finally, regression analysis provides an insight into the appropriateness of both machine learning models, as well as their comparative performance, by means of aggregate measures and individual image plots. Appendix 1 specifies the list of code - and data adjustments that need to be applied to the source files for a functional implementation of the MARGE package. Appendix 2 provides the complete coefficients of determination (R2-scores) achieved in testing by both predictive models.