Course description
With our Advanced Computer Science MSc, you will have the flexibility to tailor your learning and pursue the topics that interest you most. You will have the opportunity to choose four from eight academic themes, each of which combines two related course units.
Certain combinations are integrated into specialised pathways , including artificial intelligence, computer security, data and knowledge management, and digital biology.
Computational thinking is becoming increasingly pervasive and is informing our understanding of phenomena across a range of areas, from engineering and physical sciences, to business and society. With the Advanced Computer Science MSc, you will learn from world-leading academic staff to amplify your skills ahead of a successful career in either industry or academia.
Aims
- You will progress down your own chosen pathway, taking advantage of the flexibility of units on offer.
- You will boost your employability across nearly all areas of business and society, with the technical skills you acquire being in great demand.
Special features
Flexibility
You will choose four from eight themes, each of which combines two related course units.
Strong links with employers
We maintain close relationships with potential employers and run various activities throughout the year, including career fairs, guest lectures, and projects run jointly with partners from industry.
Excellent facilities
You will have access to a fantastic range of facilities and equipment.
Teaching and learning
Coursework and assessment
Course unit details
This course aims to impart advanced knowledge across a broad range of Computer Science, offering training in advanced skills. It is suitable for those who wish to enhance their computing skills in order to improve their contribution to IT-related industry or to pursue R&D in academia or industry.
A student following the Advanced Computer Science course chooses four from eight themes, each of which combines two related course units that build on top of each other. Certain combinations are integrated into specialised pathways . A student who opts to follow the pathways will have the pathway specialism included in their degree certificate.
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 |
---|---|---|---|
Masters Project (60 Credits) | COMP66060 | 60 | Mandatory |
Automated Reasoning and Verification | COMP60332 | 15 | Optional |
Modelling Data on the Web | COMP60411 | 15 | Optional |
Principles of Digital Biology | COMP60532 | 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 |
Cognitive Robotics and Computer Vision | COMP61342 | 15 | Optional |
Displaying 10 of 15 course units | |||
Display all course units |
Additional fee information
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.