Learning Control Systems -Review and Outlook

The basic concept of learning control is introduced. The following four learning schemes are briefly reviewed: (l) trainable controllers using linear classifiers, (2) reinforcement learning control systems, (3) Bayesian estimation, and (J-i) stocha.stic approximation. Potential replications and problems for further research in learning control are outlined

Learning Non-Parametric Models with Guarantees: A Smooth Lipschitz Interpolation Approach

We propose a non-parametric regression method that does not rely on the structure of the ground-truth, but only on its regularity properties. The methodology can be readily used for learning surrogate models of nonlinear dynamical systems from data, while providing bounds on the prediction error. In contrast with the well known Set Membership and Kinky Inference techniques that yield non-differentiable functions, the approach presented herein produces a smooth regressor. Consequently, it is more suitable to optimization-based controllers that heavily rely on gradient computations. A numerical example is provided to show the effectiveness of the method we call Smooth Lipschitz Interpolation (SLI) when compared to the aforementioned alternatives in a Model Predictive Control problem

Systems biology of breast cancer

Breast cancer, with an alarming incidence rate throughout the globe, has attracted significant investigations to identify disease specific biomarkers. Among these, oestrogen receptor (ER) occupies a central role where overexpression is a prognostic indication for breast cancer. The cross-talk between the responsible contenders of ER-associated genes potentially play an important role in the disease aetiology. Investigation of such cross talk is the focus of this thesis. The development of high throughput technologies such as expression microarrays has paved the way for investigating thousands of genes at a time. Microarrays with their high data volume, multivariate nature and non-linearity pose challenges for analysing using conventional statistical approaches. To combat these challenges, computational researchers have developed machine learning approaches such as Artificial Neural Networks (ANNs). This thesis evaluates ANNs based methodologies and their application to the analysis of microarray data generated for breast cancer cases of differing oestrogen receptor status. Furthermore they are used for network inferencing to identify interactions between ER-associated markers and for the subsequent identification of putative pathway elements. The present thesis shows that it is possible to identify some ER-associated breast cancer relevant markers using ANNs. These have been subsequently validated on clinical breast tumour samples highlighting the promise of this approach

Online Learning Models during the COVID-19 Pandemic: A Bibliometric Analysis

Online learning as a method of avoiding the spread of COVID-19 adds a new dimension to the fight against this virus. The first publication with online learning as a topic can be traced back to  1986, An Algorithm for Learning Without External Supervision and Its Application to Learning Control Systems (Nikolić & Fu, 1986). Online learning in the education sector has become increasingly prominent in recent years. The study aims to discover and illustrate the direction of trend development of research on online learning during COVID-19. The study used the bibliometric analysis method. It examined 1,594 scientific articles from 159 authors across 110 countries and 160 affiliations. The information gathered between 2019 and 2021 was relevant to the topic. To visualize the result of the study, the researcher used the VOSViewer application. The study shows that research on online learning during the COVID-19 pandemic increased consistently from 2019 to 2021. This study found 1249 documents in that period. Yang H is the most co-authored author. While the most prolific writer is Hong J.C, with 8 articles. This systematic field mapping helps in graphically depicting the evolution of publications as well as identifying areas of current research interest and future research potential. These findings provide a solid basis for future research in this area