8498 modules
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STAT6135 2025-26
Generalised Linear Models
This module aims to introduce students to a wide range of statistical models grouped by the unifying theory of generalized linear models: linear, logistic, multinomial, cumulative ordinal and Poisson regression, as well as log-linear models are presented, with emphasis on the underpinning theory and practical examples. Students are also exposed to the basic foundations of estimation for GLMs. -
STAT6135 2026-27
Generalised Linear Models
This module aims to introduce students to a wide range of statistical models grouped by the unifying theory of generalized linear models: linear, logistic, multinomial, cumulative ordinal and Poisson regression, as well as log-linear models are presented, with emphasis on the underpinning theory and practical examples. Students are also exposed to the basic foundations of estimation for GLMs. -
SSPC6906 2026-27
Generative AI
In this module you will explore Generative AI, the recent advance in AI system capability that produces text, images, video, audio or software code in response to a user’s prompting. The module explains some of the novel techniques used in this form of AI (e.g. large language models, transformers, attention) and gives you the opportunity to use generative AI to solve specific problems with hands-on experience of deployment through training large language models (LLMs), prompt engineering and fine tuning.
Students will develop a responsible AI perspective by examining the problems, limitations , economic and human costs of these technologies in terms of hallucinations, energy consumption, intellectual property violations, the social impact on labour markets and challenges to the cultural sector.
The module will also address the claims made by technology leaders concerning the future evolution of Generative AI chat-bots into Artificial General Intelligence (AGI), and the so-called “existential” risks of AI. -
SSPC6906 2025-26
Generative AI
In this module you will explore Generative AI, the recent advance in AI system capability that produces text, images, video, audio or software code in response to a user’s prompting. The module explains some of the novel techniques used in this form of AI (e.g. large language models, transformers, attention) and gives you the opportunity to use generative AI to solve specific problems with hands-on experience of deployment through training large language models (LLMs), prompt engineering and fine tuning.
Students will develop a responsible AI perspective by examining the problems, limitations , economic and human costs of these technologies in terms of hallucinations, energy consumption, intellectual property violations, the social impact on labour markets and challenges to the cultural sector.
The module will also address the claims made by technology leaders concerning the future evolution of Generative AI chat-bots into Artificial General Intelligence (AGI), and the so-called “existential” risks of AI. -
MEDI6235 2025-26
Genomic Informatics
This module is only compulsory for the MSc Genomics (Informatics) pathway, and optional for other pathways.
This module will allow students to develop skills in analysis of data generated by different omic technologies, particularly giving experience in the analysis of transcriptomic and cancer genomic data using command line tools. -
MEDI6235 2026-27
Genomic Informatics
This module is only compulsory for the MSc Genomics (Informatics) pathway, and optional for other pathways.
This module builds on the knowledge and experience gained from the Genomic Technologies and Basic Informatics module, introducing the Linux command line environment. Students will perform analysis of data generated by different omic technologies, particularly transcriptomic and cancer genomic data. Upon completing this module students will be in a strong position to base their MSc research project on NGS data. -
MEDI6237 2025-26
Genomic Technologies and Basic Informatics
This module explores the state-of-the-art genomics techniques used for DNA sequencing (e.g. targeted approaches, whole exome and whole genome sequencing) and RNA sequencing, together with current technologies routinely used to investigate genomic variation in both clinical and research settings.
The module will cover the fundamental principles of informatics and bioinformatics applied to genomics. The students will be taught to find and use major genomic and genetic data resources; use software packages, in silico tools, databases and literature searches. Specifically, students will learn to align sequence data to the reference genome, critically assess, annotate and interpret findings from genetic and genomic analyses. Theoretical sessions will be coupled with practical assignments of analysing and annotating predefined data sets.
A comprehensive introduction to the functional interpretation of genomic data will be included. Students will also learn about the strategies employed to evaluate pathogenicity of variants for reporting and acquire the skills to analyse genomic data in a graphical user interface (GUI). -
MEDI6237 2026-27
Genomic Technologies and Basic Informatics
This module explores the state-of-the-art genomics techniques used for DNA sequencing (e.g. targeted approaches, whole-exome and whole-genome sequencing) and RNA sequencing, together with current technologies routinely used to investigate genomic variation in both clinical and research settings.
The module will cover the fundamental principles of informatics and bioinformatics applied to genomics. Theoretical sessions will be coupled with practical assignments of analysing and annotating predefined data sets.
A comprehensive introduction to the functional interpretation of genomic data will be included. Students will also learn about the strategies employed to evaluate pathogenicity of variants for reporting and acquire the skills to analyse genomic data in a graphical user interface. -
MEDI6238 2025-26
Genomics Dissertation
The dissertation module provides a context within which research skills can be developed. It provides the opportunity to apply and demonstrate the skills and knowledge acquired throughout the taught component of the MSc programme.
The dissertation is a hypothesis-driven small-scale empirical research project involving quantitative or qualitative research methods and may be laboratory based, data analysis/bioinformatics or a systematic literature review. Students are supported to identify a project and supervisors aligned to their interests.
Dissertation projects are undertaken over a period of approximately 4 months (full-time students) or 10 months (part-time students), with submission in late September. -
MEDI6238 2026-27
Genomics Dissertation
The dissertation module provides a context within which research skills can be developed and provides the opportunity to apply and demonstrate the skills and knowledge acquired throughout the taught component of the MSc Genomics programme.
The dissertation is a hypothesis-driven small-scale empirical research project involving quantitative or qualitative research methods and may be laboratory based, data analysis/bioinformatics or a systematic literature review. Students are supported to identify a project and supervisors aligned to their interests.
Dissertation projects are undertaken over a period of approximately 4 months (full-time students) or 10 months (part-time students), with submission in September.