This course provides an introduction to the key concepts of machine learning (ML) and its applications in Bioinformatics for second- or third-year students who are interested in multidisciplinary research and applications. The course will not have any prerequisites. It will equip students with essential machine learning skills and allow them exploring a new career or research area in the future. Practical examples will be used to demonstrate the concept, algorithms and real-world applications.AIMS: To introduce the fundamentals of ML, including knowledge representation, modelling, learning and evaluation; To develop the capability in applying the knowledge and tools to build ML models for various types of problems in computational biology; To develop the skills for solving problems in Bioinformatics using ML TEACHING FORMAT: Lectures will be delivered in Jupyter Notebooks; Scikit-learn and python will be used in lectures, practicals, and assessmentsLEARNING OUTCOMES: Successful students will be able to:Demonstrate understanding of the fundamentals in the core areas of ML such as knowledge representation, modelling, learning and evaluation;Develop ML models for various types of problems in Bioinformatics including classification and clustering; and evaluate them using a metric of choice;Employ ML techniques to solve practical problems in the real word;Exercise self-motivation and original thinking.