The human gut microbiome has emerged as a key player in health and disease, yet machine learning on microbiome data remains challenging due to its high dimensionality, sparsity, compositionality, and inter-study heterogeneity. Although classical and deep learning methods have demonstrated promise, they often require extensive labeled data, which is rarely available in microbiome research. In this thesis, we investigate whether meta-learning can address these challenges by enabling better generalization from small, heterogeneous microbiome datasets. Specifically, we benchmark Prototypical networks (Protonets), a metric-based, few-shot meta-learning algorithm, against strong classical baselines (Random Forests, XGBoost, and Multi-layer Perceptrons) for disease classification tasks across a selected number of gut microbiome studies. We introduce a unified benchmarking pipeline that standardizes preprocessing, dimensionality reduction, task construction, and evaluation across studies. A leave-one-study-out cross-validation strategy simulates realistic deployment scenarios where only a few labeled samples are available from a new cohort. Our experiments explore the impact of support set size and dimensionality reduction via principal component analysis. Results show that although Protonets offer a conceptually appealing approach for few-shot learning, they consistently underperform compared to Random Forests in classification accuracy. Statistical analyses confirm the significance of this performance gap, and embedding visualizations reveal limited class separation in the learned feature space. These findings suggest that, under the evaluated conditions, classical models like Random Forests remain the more robust choice for microbiome classification in low-data regimes. By offering a rigorous and reproducible evaluation, this work lays the foundation for further exploration of meta-learning in microbiome research and highlights both the potential and current limitations of learning to learn in this complex domain.

Bayesian Optimization (BO) has demonstrated significant utility across numerous applications. However, due to it being designed as a universal optimizer, its performance can often be suboptimal in specialized environments. To overcome this issue, research has been conducted into the application of transfer learning for enhancing BO performance in these specialized contexts. This paper describes the research done into evaluating the MetaBO algorithm in some specific environments. MetaBO innovates by substituting the acquisition function component in BO with a neural network that serves as an acquisition function, trained via a reinforcement learning framework. Although the results indicate that the algorithm's performance is not optimal in the environments tested, these limitations are ascribed to elements of the implementation rather than the concept of the algorithm itself. Consequently, further research is necessary to refine the implementation process and fully exploit the potential of the MetaBO algorithm.

To make open door policies easier in nursing homes, it is important to have a smart system that can check whether access is allowed for the person trying to enter some place or not. This thesis documents how an access control system has been built, for Momo Medical, to be used at a nursing home. It sheds light on hardware considerations made and the software written for the final product. All the knowledge gathered and the time spent has led to a final product that with minor tweaks can be used in the existing infrastructure of Momo Medical but also without the presence of it. There are however still improvements left to be made for the final product to be commercially appealing.