Handbook

Machine learning is the algorithmic approach to learning from data. The course also covers aspects of data mining, the application of machine learning to obtain insight from data. In this course machine learning algorithms are placed in the context of their theoretical foundations in order to understand their derivation and correct application. Machine learning also is an empirical science, where performance of algorithms must be rigorously evaluated on datasets. Completion of this course will contribute to further learning in advanced topics such as deep learning, bioinformatics, computer vision, and robotics. Topics covered in the course include: linear models for regression and classification, local methods (nearest neighbour), tree learning, kernel machines, neural networks, unsupervised learning, ensemble learning, and learning theory. To expand and extend the development of theory and algorithms presented in lectures, practical examples will be given in tutorials and programming tasks during the project.