Aims:

The overarching aim will be to introduce students to the most current research in machine learning. This will be of particular value to students planning to undertake PhD or to Industrial Research.

Learning outcomes:

On successful completion of the module, a student will be able to:

Evaluate the differences between the various methods that achieve the goal of this particular topic and thus understand their strengths and weaknesses.
	Implement an algorithm that achieves the aim of this particular topic.
	Use the appropriate to implement the algorithm for example tensorflow for deep neural network and CVXOPT for the convex optimization approach.
	Develop research skills that thus enable them to critically evaluate competing research papers that address the same problem (topic) and devise experiments as well as theoretical arguments enabling insightful comparison between methodologies.
For example, if the topic was “meta-learning”, students would learn both deep neural network and convex optimization approaches to this problem, the types of scenario when the deep network was successful and the convex optimisation approach was not and vice versa.

Content:

This module is designed to introduce students to the most current research topics in machine learning. Such topics will correspond to “trending” topics within the last five years as represented in International Machine Learning Conferences. The backbone of the course will be a series of lectures on a given set of selected topics for that year. As appropriate this may be supplemented by seminar-style class work where current research papers are read in common, discussed, and presented.

There will be many potential “trending” topics that can be the subject for any given year, for example,

Fairness and Artificial Intelligence
	Scalable Gaussian processes
	Modern variational inference
	Meta-Learning
	Bayesian Optimisation
	Multi-task Learning
	Mirror Descent
	Probabilistic Programming
	Inductive Logic Programming
	Machine Learning and Physical Sciences
	Privacy-preserving Machine Learning
	Submodular optimization
To name a few.

Requisites:

To be eligible to select this module as an optional or elective, a student must be registered on a programme and year of study for which it is a formally available.