Machine Learning (ML)

Description

This course focuses on data preparation, supervised and unsupervised learning. The preparation and use of datasets for learning tasks will be presented, with a focus on pre-processing for efficiency. This includes de-noising, sampling, feature extraction and normalization.The course covers basic supervised and unsupervised learning algorithms, identifying their uses and limitations and learning how to implement and evaluate them. The supervised learning models introduced include k-NN, Naïve Bayes, random forest and ensemble methods. In terms of unsupervised methods, different clustering techniques will be introduced. The theory focuses on the required notions to understand optimisation, measures and dimensionality reduction.