The English language requirements for our programmes are indicated by English bands, and therefore the specific test and score acceptable is based on the band assigned to the academic department within which your chosen course of study is administered. Note that for some academic departments there are programmes with non-standard English language requirements.
The English Language requirements for entry to postgraduate taught and research programmes in the School of the Arts fall within the following English band:
Band 5: IELTS (Academic) minimum score 7.0 overall with 6.0 in each of Writing, Listening, Reading and Speaking
We accept a range of English tests and qualifications categorised in our English bands for you to demonstrate your level of English Language proficiency. See all accepted English tests that we deem equivalent to these IELTS scores.
This program builds a solid foundation in AI methods for language data, equipping you with skills in processing, analyzing, and visualizing large datasets. You'll gain expertise in statistical computing using R, Python programming, and computational modeling for language applications. Key topics include: Core computational linguistics concepts and advanced AI architectures (e.g., Hidden Markov Models, RNNs, LSTMs, Transformer architectures). Linguistic properties of text (word structure, sentence meaning, discourse cohesion) and their computational applications. Machine learning systems for language processing, their advantages, limitations, and future directions. You'll learn to write Python programs for data acquisition, cleaning, and analysis, with a focus on Humanities and Social Sciences datasets, while also understanding the role of other programming languages like Java. The program integrates theoretical and practical aspects of computational approaches in linguistics, supported by the department's expertise in language analysis and ethics. Advice for UK and EU students Advice for international students
This programme will build your digital AI knowledge and skills and provide a solid foundation in AI methods applied to language data.
You will understand how to apply your knowledge to real-world large datasets. You will develop the skills to understand, organise, clean, process, and analyse a range of data types. You will use the software R to develop skills at statistical computing and data visualisation.
You will gain an understanding of how the various linguistic properties of text (word structure, sentence structure, word and sentence meaning, and discourse structure) are important in the computational processing of text, and how current computational models apply to extract information relevant to these properties. You will understand how machine learning systems work in this domain, what their advantages and limitations are both as theories and as tools, and where the field is currently heading.
You will learn advanced concepts and algorithms in computational linguistics underlying contemporary generative AI models, for language. It covers core computational architectures (Hidden Markov Models, Recurrent Neural Networks, Long-Short Term Memory networks, Transformer architectures for LLMs), advanced linguistic concepts (constituency, ambiguity, dependency, semantic roles, affect, anaphora, and discourse cohesion) and how computational approaches, both symbolic and neural, can be applied to these.