Aims and Fit of Module

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

Learning outcomes

CODE OUTCOME
A Have a solid understanding of the theoretical issues related to problems that machine learning algorithms try to address.
B Be able to ascertain the properties of existing ML algorithms and new ones.
C Be able to apply ML algorithms for specific problems.
D Be proficient in identifying and customising aspects on ML algorithms to meet particular needs.

Method of teaching and learning

Students will be expected to attend two hours of a formal lecture and one hour of a lab section in a typical week. Lectures will introduce students to the academic content. Labs will be used to code the lecture materials in Python using ML Libraries. 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. Two assessments 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.