Machine learning is one of the main concepts behind data science and artificial intelligence (AI). The term machine learning or statistical learning refers to the science of automated detection of patterns in data. It has been widely used in tasks that require information extraction from large datasets. Examples of tasks include SPAM detection, fraudulent credit card transaction detection, face recognition by digital cameras, and voice commands recognition by personal assistance on smart-phones. Machine learning is also widely used in scientific domains such as bioinformatics, medicine, and astronomy. One characteristic of all these applications is that a human developer cannot provide an explicit and detailed specification of how these tasks should be executed, due to the complexity of the patterns that need to be detected.
This course aims to introduce the main concepts underlying machine learning, including for instance, (a) what is learning, (b) how can a machine learning, (c) what kind of problems can be solved by using machine learning approach, (d) how to formalise them as a machine learning problem, and (e) how to compare and evaluate the performance of different machine learning. We will focus on methods that are successfully used in practice, including regression, supervised and unsupervised techniques, and neural networks.