Each tutorial group (maximum 4 students) is allocated to a tutor, usually an advanced PhD student, who meets weekly for an hour to discuss set texts; to...

Philosophical Study Skills: Reading, Understanding and Essay Writing (PHIL0008)

Each tutorial group (maximum 4 students) is allocated to a tutor, usually an advanced PhD student, who meets weekly for an hour to discuss set texts; to improve the students’ understanding of them; to debate; and to instruct the students in essay-writing skills. Each student in the group will write some formative essays in the course of the term and may in addition be required to prepare presentations. The tutor is responsible for deciding – according to their expertise - the selection of texts to be studied, although this is done in consultation with the course convenor. See below for examples. In general, there will be a spread of different philosophical texts, possibly on a common theme, and frequently including both ‘historical’ and more contemporary texts. Assessment method 2,000-word submitted essay, due in on the first day of the following Term. It is possible to work up and submit one of the essays presented earlier in the term, in the light of feedback from the tutor.

Philosophy Area n/a

This module is for all professionals and practitioners who are interested in promoting health and wellbeing through education in its broadest sense whether...

Promoting Health and Wellbeing: Planning, Practice and Participation (EDPS0055)

Aims:

The module aims to enable you to accumulate work experience in AI on a real-world problem within the biomedical application realm from diagnosis and...

MSc Artificial Intelligence for Biomedicine and Healthcare Project (COMP0192)

Aims:

The module aims to enable you to accumulate work experience in AI on a real-world problem within the biomedical application realm from diagnosis and disease prognosis to clinical services workflow delivery. 

The aims are to: 

Provide students the opportunity to apply learned machine learning and artificial intelligence methodologies to the biomedical and healthcare domain.  
	Capacitate students to propose and design ML and AI solutions to solve specific real problems in the biomedical and healthcare field. 
	Capacitate students to be effective team players in interdisciplinary research groups within the biomedical and clinical sciences.
Learning outcomes:

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

Assimilate the project-specific biomedical terminology, its relation to clinical pathways and the challenges in clinical decision support. 
Design, implement and evaluate AI and ML algorithms to solve real-world clinical problems. 
Interpret and analyse the challenges of healthcare datasets. 
Evaluate the ethical implications of using AI to improve healthcare.
Content:

This three-month individual-project structure is aimed to consolidate our students’ knowledge in Artificial Intelligence (AI) and Machine Learning (ML), acquired during first and second terms of the programme, by immersing them within the biomedicine and healthcare domain environment.  

Students will learn how to apply their acquired knowledge to design, implement and evaluate an AI solution to attempt to solve a real biomedical and healthcare challenge.  

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 formally available.