- Entry requirements: Related 2:1 degree (or equivalent)
- Full-time: 12 months
- Part-time: 24 months
This course aims to extend your knowledge gained during undergraduate study with more advanced specialised material reflecting current research at the “cutting-edge” of the discipline.
This programme will underpin and enhance your current knowledge and understanding; along with skills that you develop during the programme, will provide you with a strong basis for your future career in the IT industry and towards specialisation in the field of Computer Science related research and development.
Designed for graduates of the highest calibre, the MSc in Advanced Computer Science is directed at graduates with a previous Computer Science or IT degree.
The programme is accredited by the British Computer Society and is continually updated to reflect new technologies and trends.
Discover what you'll learn, what you'll study, and how you'll be taught and assessed.
International students may be able to study this course on a part-time basis but this is dependent on visa regulations. Please visit the Government website for more information about student visas.
If you're able to study part-time, you'll study the same modules as the full-time master's degree over a longer period, usually 24 months. You can make studying work for you by arranging your personal schedule around lectures and seminars which take place during the day. After you complete all the taught modules, you will complete your final dissertation or project and will celebrate your achievements at graduation the following term.
Studying part-time means you can study alongside work or any other life commitments. You will study the same modules as the full-time master's degree over a longer period, usually 24 months. You can make studying work for you by arranging your personal schedule around lectures and seminars which take place during the day. After you complete all the taught modules, you will complete your final dissertation or project and will celebrate your achievements at graduation the following term.
Your only compulsory module this semester will help you learn and practice all the necessary skills needed to conduct independent research in computer science which you will need for further learning and your final projects.
You will then select at least three optional modules for the remainder of the semester. You can choose to focus on practical algorithms and data structures for large datasets, how the modern geographic information sciences toolkit can be integrated with Data Science tools to solve practical real-world problems, the fundamentals of how images are generated, represented, compressed and processed, parallel programming for multi-core architectures, optimisation methods, or privacy and security topics such as identification and authentication, monitoring protocols, attacks and defences, legal and ethical issues and future directions.
In this module the students will learn and practise all the necessary skills needed to conduct independent research in computer science, including literature search, project management, presentation techniques, peer reviewing, writing skills and critical review of texts. They will also learn about the professional, legal, social and ethical framework of the IT industry. The module covers, e.g., planning and scheduling projects and drawing Gantt charts. Students shall also conduct a research project (including research, paper, literature review, or MSc project proposal, …) and use tools like EndNote and Zotero bibliography manager within MS Word and Latex.
The module introduces formalisms to reason about knowledge and information. One such formalism is epistemic logic, where one can explicitly represent of what an agent (robot, human, system) knows about the world or about others, as in "I have sent a message, how do I know that it has been received, and that the receiver knows I know this?"
The module "Privacy and Security" covers topics such as: identification and authentication, monitoring protocols, attacks and defences, legal and ethical issues and future directions.
Biology inspired adaptive algorithms such as Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) play an important role in modern computing, information processing, and machine learning. The latest increase in computer power ensured broad use of the algorithms to solve problems in science and engineering previously considered impossible to tackle. ANNs are now broadly used in pattern recognition, including speech recognition and classification problems, statistics, functional analysis, modelling financial series with considerable stochasticity, etc. GAs are search procedures based on the mechanics of natural selection and natural genetics. They provide effective solutions to a variety of optimisation problems in economics, linguistics, engineering, and computer science. Both ANNs and GAs can exploit massively parallel architectures to speed up problem solving and provide further understanding of intelligence and adaptation.The main goals of the module are to introduce students to some of the established work in the field of Artificial Neural Networks and Genetic Algorithms and their applications, particularly in relation to multidisciplinary research. To equip students with a broad overview of the field, placing it in a historical and scientific context. The module provides students with the knowledge and skills necessary to keep up-to-date in actively developing areas of science and technology and be able to make reasoned decisions.
Masters module on practical algorithms and data structures for large datasets.
This is a module to cover theoretical and practical aspects of parallel programming for multi-core architectures with the main focus on hand-on programming experience with latest multi-core and multi-processor platforms.
This module will introduce students to the nascent field of Geographic Data Science (GDS), a discipline established at the intersection between Geographic Information Science (GIS) and Data Science.
The course covers how the modern GIS toolkit can be integrated with Data Science tools to solve practical real-world problems specifically the programming language ‘Python’, which is the only scripting langauge officially supported by the industry-leading GIS packages ‘Arc/GIS’ and ‘QGIS’.
Core to the set of employable skills to be taught in this course is an introduction to programming tools for GDS.
The programme of lectures, guided practical classes and independent study illustrate how and why Geographic Data Science is useful for social science applications.
This module covers the fundamentals of how images are generated, represented, compressed and processed to extract features of interest.
This module is an in-depth tour over optimisation methods applied for various optimisation models. These methods are extensively used in both academic and industrial practices.
You have the chance to choose all of your modules during your second semester, giving you the opportunity to have a bespoke experience.
You could dig into basic algorithmic methods for the design and analysis of algorithms, the algorithmic aspects of game theory, biologically inspired optimisation and introduction to neural networks for artificial intelligence, machine learning, data mining, source coding and error correcting, multi-agent systems, utilising advanced web technologies, use of logic as a tool for specifying the desired behaviour of hardware, software and artificial intelligence systems, game-theoretic discussions of auctions, technical and organisational discussions about cryptography and security, or gain an understanding of how maps can be visualised online.
You’ll also have the chance to participate in a group project where you can work with colleagues as a programming team to build on work from your first semester to find a solution to a relevant problem.
This module aims to teach basic algorithmic methods for design and analysis of algorithms.
Safety and Dependability will cover techniques for the validation of systems against formal specifications. In a first part, safety specifications (something bad never happens) using the Hoare calculus and safe abstraction are covered. A second part refers to termination (something good eventually happens), exploiting well foundedness. In a third part, Markov chains and decision processes are studied, extending the qualitative safety and termination problems from the first part to qualitative/probabilistic properties, and extending them to a simple probabilistic specification language, PCTL. As part of the module, the ability of formulating (probabilistic) models as Markov chains and decision processes are taught, as well as the use of of-the-shelf tools like PRISM or IscasMC for their analysis.
To provide an in-depth, systematic and critical understanding of some of the current research issues at the forefront of the academic research domain of data mining. As part of the module students program with Python selected data mining algorithms and experiment using real-world datasets. Google search framework and IBM Watson QA system and various other industrial level data mining applications are discussed.
Skills: Communication skills (listening andquestioning, respecting others, contributing to discussions, communicating in a foreign language, presenting own work in form of a talk) This skill is not evaluated in the module. However, students are encouraged to verbally participate in the numerous in-class quizzes about data mining concepts.
Two Python programming assignments (accounting for 25% of the total mark for the module) arecirculated. The students are expected to implement a selected group of data mining algorithms from the scratch by themselves and experiment using real-world datasets.
Business and customer awareness (basic understanding of the key drivers for business success – including the importance of innovationand taking calculated risks – and the need to provide customer satisfaction and build customer loyalty) Google search framework, IBM Watson QA system and various other industrial level data mining applications are discussed in the class as specific implementations of the algorithms introduced in the module.
Information Technology (IT) skills (IT skills, including familiarity with word processing, spreadsheets, file management, use of internet search engines, use of specific software and/or IT and programming paradigms) Students are required to use industry-level data processing libraries such as numeric python library, scientific python library and scikit-learn machine learning library during the lab sessions.
Computer science principles
Examples: Formal tools for building and verifying complex electronic-commerce systems (name some concrete software). Formal methods for deriving classification algorithms that focus on different loss functions such as the cross-entropy loss (logistic regression), hinge loss (support vector machines) are taught in the module.
Multi-agent systems have emerged as one of the most important areas of research and development in information technology in the 1990s. A multi-agent system is one composed of multiple interacting software components known as agents, which are typically capable of co-operating to solve problems that are beyond the abilities of any individual member. Multi-agent systems are important primarily because they have been found to have very wide applicability, in areas as diverse as industrial process control and electronic commerce. This module will begin by introducing the student to the notion of an agent, and will lead them to an understanding of what an agent is, how they can be constructed, and how agents can be made to co-operate effectively with one another to solve problems.
In this module we discuss a number of topic that are important for e-commerce. These include game-theoretic discussions of auctions, technical and organisational discussions about cryptography and security, and the legal and moral issues related to privacy.
This modules provides a basic introduction to the main principles behind representing and retrieving knowledge effectively on the Web. The module covers the evolution from the standard Web to the Semantic Web, and gives student the opportunity to gain an awareness of the main methods and techniques, including practical awareness, of the main issues arising in annotating web pages with semantic information, in interlinking pages with similar semantic content and in effectively querying these pages.
This module focuses on algorithmic aspects of game theory. A main focus of this module is on the computational aspects in the design of mechanisms and auctions. as part of the module, the students learn about Googles sponsored serarch auctions, which is one of the most successful targeted advertising systems today.
This module is aimed to provide an extensive overview of the information theory and coding. Different source codes and channel codes are discussed. Cryptography is also covered.
This module teaches you about bio-inspired algorithms for optimisation and machine learning. The algorithms are based on reinforcement learning, DNA computing, brain or neural network models, immune systems, the evolutionary version of game theory, and social insect swarm behaviour such as ant colonies and bee colonies. These techniques are extremely useful for searching very large solution spaces (optimisation) and they can be used to design agents or robots that have to interact and operate in dynamic unknown environments (e.g. a Mars rover, a swarm of robots or network of satellites). The idea of learning optimal behaviour, rather than designing, algorithms and controllers is especially appealing in AI.
The module introduces the student to the use of logic as a tool for specifying the desired behavior of hardware, software and artificial intelligence systems, and for checking whether a given system does indeed behave as desired. The module enables the student to gain familiarity with a set of techniques which are critical in contemporary industrial applications and in academic research. It consists of 30 lectures and 10 practical sessions.
Through this module students will gain an understanding of how maps can be visualised online through a number of web platforms. Additionally, the internet will be presented both as a source of new data, and provide analytical functionality that can assist when solving geographic problems. Geographic data can be any dataset that can be visualised in a map. The module is taught through a mixture of lectures and practicals, and is assessed through two summative projects.
Biologically inspired optimisation and introduction to neural networks for artificial intelligence.
Your final project will give you the opportunity to work independently to explore a substantial problem in depth, making practical use of principles, techniques and methodologies you have acquired during the programme.
You will create a proposal, a presentation, and a final dissertation.
Masters Level final project (individual project with dissertation)
You will learn using a combination of formal lectures, small group tutorials and practical sessions in our state-of-the-art PC and Mac laboratories. Throughout the year, you will also take part in one or more group projects. At the end of the year, you will complete a large individual project.
As well as subjects in computer science, you will also develop general skills required for employability in industry or research including teamwork, presentation skills and research techniques.
Modules are assessed through a combination of examinations and coursework. You will sit examinations at the end of each semester, which are typically in-person written assignments, usually completed over 2 or 2.5 hours. You will complete coursework throughout the semester, typically class tests, programming assignments or small projects.
Lastly, you will submit a final dissertation assessed through a combination of written reports and a presentation of your achievements.
We have a distinctive approach to education, the Liverpool Curriculum Framework, which focuses on research-connected teaching, active learning, and authentic assessment to ensure our students graduate as digitally fluent and confident global citizens.
Studying with us means you can tailor your degree to suit you. Here's what is available on this course.
The Department of Computer Science (with history going back to the 1960s) is a Centre of Excellence for teaching and research. The latest Research Excellence Framework rated 97% of our research outputs as being world-leading or internationally excellent, the highest proportion of any Computer Science department in the UK.
Dr Terry Payne talks you through what you can expect studying Computer Science at the University of Liverpool and shows you some of the facilities and equipment you will be using.
From arrival to alumni, we’re with you all the way:
The longer you stay in higher education, the more qualified you’ll be and the more fun you’ll have along the way. You will find it to be the best decision you ever made, after deciding to study for your Bachelor’s degree. I found my undergraduate years to be the best of my life, so doing an extra year at master’s level only prolongs the enjoyment!
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Graduating in Advanced Computer Science will provide you with a basis for further career development towards senior technical and managerial positions in the IT industry, and towards specialisation in the field of Computer Science-related research and development.
Job titles and their definitions are not standardised within the IT industry and in a fast changing world employers demand maximum flexibility. However the following are some current options:
Your tuition fees, funding your studies, and other costs to consider.
|UK fees (applies to Channel Islands, Isle of Man and Republic of Ireland)|
|Full-time place, per year||£12,100|
|Part-time place, per year||£6,050|
|Full-time place, per year||£26,350|
|Part-time place, per year||£13,175|
Tuition fees cover the cost of your teaching and assessment, operating facilities such as libraries, IT equipment, and access to academic and personal support.
If you're a UK national, or have settled status in the UK, you may be eligible to apply for a Postgraduate Loan worth up to £12,167 to help with course fees and living costs. Learn more about tuition fees, funding and Postgraduate Loans.
We understand that budgeting for your time at university is important, and we want to make sure you understand any course-related costs that are not covered by your tuition fee. This could include buying a laptop, books, or stationery.
Find out more about the additional study costs that may apply to this course.
We offer a range of scholarships and bursaries to help cover tuition fees and help with living expenses while at university.
The qualifications and exam results you'll need to apply for this course.
My qualifications are from: United Kingdom.
|Postgraduate entry requirements||
You will normally need a 2:1 honours degree, or above, or equivalent. This degree should be in computer science or a closely related subject.
A Pre-Master's course, which prepares international students for postgraduate study with us, is offered by the University of Liverpool International College. If you hold a bachelor's degree or equivalent, but don't meet our entry requirements, successfully completing the relevant Pre-Master's pathway means you're guaranteed a place on your chosen course.
You'll need to demonstrate competence in the use of English language. International applicants who do not meet the minimum required standard of English language can complete one of our Pre-Sessional English courses to achieve the required level.
|English language qualification||Requirements|
View our IELTS academic requirements key.
Standard Level 5
|TOEFL iBT||88 or above with minimum scores in components as follows: Listening and Writing 17, Reading 17, Speaking 19.|
|INDIA Standard XII||70% or above from Central and Metro State Boards|
|Hong Kong use of English AS level||C|
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Liverpool bursts with diversity and creativity which makes it ideal for you to undertake your postgraduate studies and access various opportunities for you and your family.
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Dr Louwe Kuijer
Last updated 23 March 2023 / / Programme terms and conditions /