Module Content and Indicative Topics 

The question of how we define equality, who counts as equal or whether a society can be completely egalitarian in...

This module offers a comprehensive overview of the grammar of contemporary English. During the first term we start with the basic building blocks of word...

This module offers a comprehensive overview of the grammar of contemporary English. During the first term we start with the basic building blocks of word classes, phrases and clauses, as well as grammatical functions. During the second term we discuss more complex syntactic structures. A major feature of the module is that you will be trained to apply the principles of syntactic argumentation. 

This 30-credit module is compulsory for all students on the MA in English Linguistics. It is taught in two-hour long weekly seminars over two terms, and is assessed by a three-hour written exam.

This module aims to identify and explore some of the major perspectives in the sociology of education, bringing together a team of experts to explore a range...

This module aims to identify and explore some of the major perspectives in the sociology of education, bringing together a team of experts to explore a range of contemporary issues in this exciting and diverse field. We explore the relationships between education, society, the state and the individual, and consider how forces like globalisation and marketisation are impacting education. We pay particular attention to educational (in)equalities relating to social class, race, ethnicity, religion, gender, sexuality, migration and citizenship. Over the course of the module you will discover how key theorists and bodies of research in sociology of education can help us to better understand and respond to pressing educational issues. This module can be taken face-to-face (autumn term).

Aims:

The aims of the module are to provide familiarity with Bayesian approaches to deep learning, currently a very active area of research....

Aims:

The aims of the module are to provide familiarity with Bayesian approaches to deep learning, currently a very active area of research. This complements material covered in Introduction to Deep Learning (COMP0090).

Learning outcomes:

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

Identify both the benefits and difficulties posed by Bayesian approaches deep learning.
	Implement a number of different approaches to Bayesian neural networks for classification and regression and understand the trade-offs between methods.
	Recognize the key concepts related to uncertainty quantification in predictive models.
	Implement approaches to using deep learning for generative modeling and unsupervised learning.
	Identify the fundamental connections between both deep learning and probabilistic modelling.
Content:

This module covers topics on Bayesian approaches to deep learning, including theory and applications. We focus on understanding the role of uncertainty in deep learning models in both supervised and unsupervised settings.

Topics include:

Bayesian treatments of model parameters.
	Uncertainty quantification.
	Inference in deep generative models.
	Applications which benefit from a Bayesian approach, including active learning and semi-supervised learning.
Requisites:

To be eligible to select this module as an optional or elective, a student must: (1) be registered on a programme and year of study for which it is formally available; (2) have a strong understanding of Linear Algebra, Multivariable Calculus, Probability Theory; and (3) have taken at least one introductory machine learning module, for example Supervised Learning (COMP0078) or Introduction to Machine Learning (COMP0088) (or be concurrently enrolled in such a module).