This introduction course provides students with an extensive exposure to the fundamental elements of machine learning. This course will cover the classic statistical learning and the modern machine learning methods, with the focus on supervised learning. Topics cover the elementary concepts and general principles, classification, regularization, linear model, model selection, neural network models.

Note: To pass the course, apart from obtaining a minimum of 40% in the overall mark, a student must also obtain a minimum mark of 30% in both continuous assessment and examination components.