Machine Learning

This course is an introduction to machine learning and provides an overview of both theoretical and practical aspects of machine learning. The course introduces the basic concepts of machine learning and presents a range of different machine learning methods and models. The course also covers statistical and practical issues related to the design and evaluation of machine learning solutions. The course consists of two parts: **Part 1: Principles (4.5 credits)** This part introduces the background and some important applications of machine learning. The following models and topics will be covered: Supervised learning (classification and regression with methods like support vector machines, naive Bayes, K-nearest neighbours, decision trees, neural networks), unsupervised learning (clustering and dimensionality reduction with methods like k-means clustering, hierarchical clustering, principal components analysis, linear discriminant analysis, density estimation), and learning theory (PAC-learning, bias/variance trade-off, regularisation). Furthermore, important concepts in machine learning, such as generative/discriminative learning, the maximum likelihood and Bayesian learning paradigms, and parametric/non-parametric learning will be discussed. **Part 2: Practice (3 credits)** This part consists of practical assignments that introduce modern machine learning libraries and development tools. The students will apply some of the machine learning methods/models covered in Part 1 to solve machine learning problems in realistic applications.