Course description
These are exciting times in biology and medicine. The genomics revolution is opening up whole new areas of research - from new insights into how organisms function, to new understandings of disease and disease processes. Medicine is currently involved in the largest and most ambitious IT project in the world - the capture and interpretation of electronic patient records. This information will make health care much more effective and can help spot new diseases early - whilst they can still be contained and controlled. At the heart of all these developments are data and knowledge - and a real need and demand for the skills and techniques that computer scientists can bring these problem areas. Biology and healthcare now provide some of the fastest growing and most challenging areas for computer scientists to apply their skills.
The Digital Biology pathway is centered around a central theme of Biohealth Informatics. The theme is specifically designed for computer scientists without any previous experience of medicine or biology and will help you to develop the core skills needed to work or research (as a computer scientist) in these rapidly evolving fields. This core theme is complemented by a range of other themes that allow students to develop additional skills which have important applications in healthcare and biology.
With our Digital Biology MSc, you will follow one mandatory academic theme, which combines two related course units. You will also have the opportunity to tailor your learning and pursue other topics that interest you, by choosing three optional themes.
Teaching and learning
Coursework and assessment
Lectures and seminars are supported by practical exercises that impart skills as well as knowledge. These skills are augmented through an MSc project that enables students to put into practice the techniques they have been taught throughout the course.
Course unit details
For September 2024 entry, we are making several changes to our course unit offering. These changes are not yet reflected in the course unit list below, but are summarised here as follows:
Masters Project COMP66060 (60 credits)
This course unit remains mandatory but will be worth 60 credits instead of 90 credits. In this course unit you will learn about the dissertation project process, how to plan the project and how to write the dissertation, including ethical and professional considerations. We will provide you with the skills to undertake, manage and deliver a technical project in the broad field of computer science, over the course of approximately 3 months (June-August).
The following two optional course units are being introduced under a new theme, which will be available to select in Semester 2: Decision Making Under Uncertainty.
Reasoning and Learning Under Uncertainty COMP64102 (15 credits)
Machine learning is increasingly being used for decision support in data driven applications. A key concept when making decisions based on predictive models is that of uncertainty, e.g., in applications of AI where safety or trustworthiness are required. Uncertainty quantification recognises that exact predictions are often out-of-reach due to theoretical or practical limitations. This course unit studies different probabilistic machine learning models that incorporate uncertain reasoning and the mathematical concepts and algorithms required to learn such models from data.
Reinforcement Learning COMP64202 (15 credits)
Reinforcement learning (RL) looks to create machine learning models that are able to make decisions. An agent learns to achieve a goal in an uncertain, potentially complex environment. Successful real-world applications include but are not limited to robotics, control, operation research, games, economics, and human-computer interactions. This course will cover the breadth of modern model-free RL methods, discuss their limitations, and introduce various current research topics. In particular, we expect to cover the following: deep learning methodology and architectures, stabilisation of approximated value estimation, modern actor-critic methods, planning as inference, exploration with deep networks, offline reinforcement learning, deep multi-agent reinforcement learning, multi-task and meta-learning.
Course unit list
The course unit details given below are subject to change, and are the latest example of the curriculum available on this course of study.
| Title | Code | Credit rating | Mandatory/optional |
|---|---|---|---|
| Principles of Digital Biology | COMP60532 | 15 | Mandatory |
| Masters Project (60 Credits) | COMP66060 | 60 | Mandatory |
| Introduction to Health Informatics | IIDS60542 | 15 | Mandatory |
| Automated Reasoning and Verification | COMP60332 | 15 | Optional |
| Modelling Data on the Web | COMP60411 | 15 | Optional |
| Data Engineering | COMP60711 | 15 | Optional |
| Systems Governance | COMP60721 | 15 | Optional |
| Foundations of Machine Learning | COMP61011 | 15 | Optional |
| Representation Learning | COMP61021 | 15 | Optional |
| Text Mining | COMP61332 | 15 | Optional |
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Scholarships and bursaries
Across our institution, we offer a number of postgraduate taught scholarships and awards to outstanding UK and international students each year.
The University of Manchester is committed to widening participation in master's study, and allocates £300,000 in funding each year. Our Manchester Masters Bursaries are aimed at widening access to master's courses by removing barriers to postgraduate education for students from underrepresented groups.
For more information, see the Computer Science Fees and funding page or visit the University of Manchester funding for masters courses website for more information.
Facilities
- Newly refurbished computing labs furnished with modern desktop computers
- Access to world leading academic staff
- Collaborative working labs complete with specialist computing and audio visual equipment to support group working.
- Over 300 Computers in the Department dedicated exclusively for the use of our students.
- An Advanced Interfaces Laboratory to explore real time collaborative working;
- A Nanotechnology Centre for the fabrication of new generation electronic devices;
- An e-Science Centre and Access Grid facility for world wide collaboration over the internet.
- Access to a range of Integrated Development Environments (IDEs)
- Specialist electronic system design and computer engineering tools.