4.1 Introduction

4 Machine Learning

4.1 Introduction

Machine Learning (ML) has become the technology powering a wide-range of applications and services. The performance and the generalisability of ML models made them a good candidate for tackling a series of real-life problems that exhibit high complexity. Take for example the recent advances of Generative Adversarial Networks (GANs) that manage to synthesise highly realistic human faces with a small number of real-world samples [127]. Generally speaking, ML-based systems managed to achieve high success rates on problems where the classic rule-based approaches did not perform well.

Nowadays, ML has been deployed in many sectors of our everyday lives. For example, during our online shopping on Ebay or Amazon, an ML-based personalised recommender system, running in the background, proposes products according to different parameters related to the user, e.g. the history of previous purchases and the time spent looking at a specific product. In addition, the automotive industry has incorporated ML technologies into their cars to make them drive themselves without any human supervision whatsoever. Furthermore, ML-based Natural Language Processing (NLP) techniques have been developed for improving the safety of online discussion environments, e.g. to detect toxic, sarcastic, harassing and abusive content [169]. In general, ML technologies have benefited various sectors, some of them being the following: medical diagnosis [131], detection of credit card fraud [146], stock market analysis [41], bioinformatics [63], speech recognition [99], object detection [40], and robot locomotion [129].

To grasp the potential of ML algorithms, it is enough to say that many tech giants, such as Google and Amazon, offer Machine Learning as a Service (MLaaS) platforms, where the users can upload their own data to train their own ML models and solve a specific classification/prediction task. Thus, the