To equip students with a broad expertise in the basic principles, techniques, algorithms, implementation and applications of Machine Learning.
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.
Students will be expected to attend two hours of a formal lecture and two hours for either a tutorial or a lab section in a typical week. Lectures will introduce students to the academic content. Tutorials/labs will be used to expand the students 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. 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.