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Computational Linguistics Curriculum

Curriculum details

The online Computational Linguistics MSc is a future-focused, practice-led programme designed for ambitious professionals. Typically completed part-time over 2 years, the programme helps you gain the skills needed to understand and critically evaluate AI-powered language technologies by combining linguistics, computing, and responsible innovation. Through personalised academic support and industry-informed teaching, you can develop the knowledge and leadership capabilities needed to achieve your professional goals and progress your career.

Multiple entry and exit routes available. Take breaks when you need them and choose a qualification that best suits you:

Please note that occasionally we may make changes to our programme curriculum. You may not always study modules in the order they are listed here.

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Courses

Learning activities

Curriculum

The programme helps you to learn how to analyse language data, design AI-powered language technologies, apply machine learning techniques, and critically assess the opportunities and challenges presented by emerging AI systems. A substantial final project also gives you the opportunity to apply your learning to an independent research or industry-relevant challenge.

Core

Exploring the fundamental principles of linguistic analysis, this module develops students’ understanding of how language is structured and how meaning is created and interpreted. Students will examine key levels of linguistic organisation, including phonology, morphology, syntax, semantics, and pragmatics, applying theoretical frameworks to the analysis of authentic language data. The module investigates how grammatical structures encode meaning, how context shapes interpretation, and how language varies across social and communicative settings. Through critical engagement with linguistic theories and methods, students will develop the analytical skills required to investigate language systematically and independently, providing a foundation for further study of language in applied, computational, and interdisciplinary contexts.
Developing practical programming skills for language and data analysis, this module introduces students to the tools and techniques required to acquire, process, and explore data using Python. Students will learn how to collect, clean, validate, and manipulate datasets through structured and reproducible workflows, applying programming concepts to real analytical challenges. The module emphasises the use of data structures, libraries, and visualisation techniques to support exploratory analysis and evidence-based reasoning. Through hands-on engagement with data, students will develop the technical competence and critical awareness required to conduct responsible data analysis and prepare for advanced study in machine learning, natural language processing, and computational linguistics.
Exploring the use of large-scale language data in linguistic research, this module introduces corpus linguistics as a powerful methodology for investigating patterns of language use. Students will examine the principles of corpus design, construction, annotation, and analysis, considering the theoretical, practical, and ethical decisions involved in creating and using language corpora. The module develops skills in applying statistical and computational techniques to explore linguistic phenomena such as meaning, variation, discourse, and style, using a range of corpus tools and analytical methods. Through the design and execution of an independent corpus-based investigation, students will develop the ability to conduct rigorous, evidence-based research and critically evaluate language patterns in authentic datasets.
Examining the latest developments in artificial intelligence, this module explores how modern AI systems are designed, trained, and applied to address complex real-world challenges. Students will investigate contemporary approaches including machine learning, deep learning, generative AI, and intelligent agents, evaluating their capabilities, limitations, and practical applications across a range of domains. The module combines technical understanding with critical reflection, encouraging students to assess the ethical, societal, and organisational implications of AI technologies while developing evidence-based solutions to real-world problems.
Developing the foundations of modern data-driven intelligence, this module introduces students to the core concepts, algorithms, and workflows that underpin machine learning systems. Students will explore supervised and unsupervised learning techniques, learning how different algorithms can be applied to tasks such as classification, regression, clustering, and dimensionality reduction. The module examines approaches to model evaluation, validation, and performance assessment, enabling students to critically interpret results and identify the strengths and limitations of different methods. Alongside technical development, students will consider issues of fairness, bias, interpretability, and responsible AI, developing a critical understanding of the opportunities and challenges associated with deploying machine learning systems in real-world contexts.
Exploring the computational analysis of human language, this module develops students’ understanding of the methods, techniques, and tools used to process, analyse, and extract meaning from text. Students will investigate the core stages of the natural language processing pipeline, including tokenisation, text normalisation, parsing, named entity recognition, classification, and information extraction. The module examines both rule-based and statistical approaches to language processing, enabling students to evaluate their suitability for different linguistic tasks and applications. Through practical engagement with widely used NLP tools and libraries, students will develop the skills required to analyse language data and implement solutions to real-world language problems. The module also encourages critical reflection on the ethical and societal implications of NLP technologies, including issues of bias, privacy, intellectual property, and responsible innovation.
Exploring the scientific principles that underpin human interaction with technology, this module examines how cognitive, perceptual, and physical processes shape the design and use of interactive systems. Students will investigate key concepts from cognitive science, human-computer interaction, and interaction design, evaluating the frameworks and models used to understand and predict user behaviour. Through the application of user-centred design principles, students will develop and assess interactive prototypes, drawing on empirical methods to evaluate usability and user experience. The module equips students with the knowledge and practical skills required to design, test, and communicate effective technology solutions informed by an understanding of human behaviour.
Exploring one of the most significant developments in contemporary artificial intelligence and computational linguistics, this module examines the architectures, methods, and applications that underpin large language models (LLMs). Students will investigate transformer architectures, self-attention mechanisms, pre-training strategies, and the processes involved in training, fine-tuning, and deploying language models at scale. Through practical engagement with pre-trained LLMs, students will apply these technologies to a range of natural language processing tasks, critically evaluating their performance, capabilities, and limitations. The module also considers the wider ethical, social, and environmental implications of large-scale language modelling, encouraging students to reflect on issues of fairness, bias, interpretability, sustainability, and responsible AI deployment.
Serving as the culmination of the programme, this module provides students with the opportunity to undertake a substantial independent project in computational linguistics. Drawing on the theoretical, methodological, and technical knowledge developed throughout their studies, students will investigate a linguistically relevant research question or design and evaluate a computational solution to a language-related problem. Projects may involve the application of natural language processing techniques, the development of language resources or tools, or the analysis of language data using computational methods. Students will engage critically with relevant literature, select and justify appropriate methodologies, and evaluate their findings in relation to current research and practice. The module develops advanced skills in independent research, problem solving, project management, and scholarly communication, culminating in a substantial project report that demonstrates professional standards of academic and technical work.

Learn by building the technologies shaping the future of language

Study online in Moodle – a collaborative learning environment that combines academic theory with practical application. Across eight eight-week modules, you’ll engage with videos, readings, practical coding exercises, discussion forums, independent research, and optional live sessions while exploring topics such as natural language processing, machine learning, conversational AI, and language data analysis. Rather than relying on traditional exams, you’ll complete project-based assessments including programming tasks, presentations, reports, case studies, and a final independent research project that showcases your ability to solve real-world language challenges.

Flexible online learning, built around your goals

Complete coursework wherever and whenever it suits you, applying what you learn through practical NLP projects, authentic language datasets, and independent research. Explore the programme modules to discover how each course builds your technical, analytical, and linguistic expertise.

Online Student Experience

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