Aims and Fit of Module

The module aims to equip students with a broad expertise in the basic principles, techniques, algorithms, implementation and applications of Machine Learning.

Learning outcomes

A. Demonstrate a solid understanding of the theoretical issues related to problems that machine learning algorithms try to address.

B. Demonstrate understanding of the properties of existing ML algorithms and new ones.

C. Aapply ML algorithms for specific problems.

D. Demonstrate proficientcy in identifying and customising aspects on ML algorithms to meet particular needs.

Method of teaching and learning

The teaching philosophy of the module follows very much the philosophy of Syntegrative Education. This has meant that the teaching delivery pattern, which follows more intensive block teaching, allows more meaningful contribution from industry partners. This philosophy is carried through also in terms of assessment, with reduction on the use of exams and increase in coursework, especially problem-based assessments that are project focused. The delivery pattern provides space in the semester for students to concentrate on completing the assessments.
Students will be expected to attend formal lectures, seminars, tutorials and lab sessions. Students will be introduced to the academic content and have an understanding of lecture materials. In addition, students will be expected to devote unsupervised time to private study. Private study will provide time for reflection and consideration of lecture material and background reading. The written assessment will assess how well students keep up with the material presented in the lectures. A written examination at the end of the module will assess the academic achievement of students.