MSc Machine Learning

For entry in the academic year beginning September 2026, the tuition fees are as follows:

• MSc (full-time)
  UK students (per annum): £14,700
  International, including EU, students (per annum): £39,400

Further information for EU students can be found on our dedicated EU page.

The fees quoted above will be fully inclusive for the course tuition, administration and computational costs during your studies.

All fees for entry will be subject to yearly review and incremental rises per annum are also likely over the duration of courses lasting more than a year for UK/EU students (fees are typically fixed for International students, for the course duration at the year of entry). For general fees information please visit: postgraduate fees . Always contact the department if you are unsure which fee applies to your qualification award and method of attendance.

Self-funded international applicants for this course will be required to pay a deposit of £1000 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. This deposit will only be refunded if immigration permission is refused. We will notify you about how and when to make this payment.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

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 Master's Bursaries are aimed at widening access to master's courses by removing barriers to postgraduate education for students from underrepresented groups.

We also welcome the best and brightest international students each year and reward excellence with a number of merit-based scholarships. See our range of master’s scholarships for international students .

And, if you have completed an undergraduate degree at The University of Manchester, or are currently in your final year of an undergraduate degree with us, you may be eligible for a discount of 10% on tuition fees if you choose to study on a taught postgraduate course here. Find out if you're eligible and how to apply .

For more information on master's tuition fees and studying costs, visit the University of Manchester funding for master's courses website to help you plan your finances.

In the world of data analytics, preparing and managing data of often the most time-consuming task --- estimated to take up to 80% of a whole workload by many reports and surveys. This unit focuses on the essential data engineering techniques that make large-scale data processing and analysis possible and efficient. Students will explore the foundational concepts and tools used in modern data engineering, including scalable data storage systems, advanced querying methods, parallel and distributed data processing, data interpretation, and effective data retrieval strategies. Emphasis is placed not just on theory, but on hands-on, practical skills that prepare students to work with real-world data.

Prior knowledge of machine learning is needed

This unit aims to provide students with exposure to and experience of specialised technologies that support data storage, access, integration and use at scale. Data engineering relates to the processes, tools and techniques required to maximise the value that can be obtained from the data resources an individual or organisation has access to. Many of the challenges faced by data engineers have been prominent for a considerable period, and have benefited from research and development that has given rise to specialised techniques for obtaining value from data. This unit aims to provide potential data engineers with the ability to select, evaluate and apply data engineering technologies to problems that involve complex data at scale.

1. Describe technologies that underpin scalability in data intensive systems and their properties.

2. Describe and discuss data integration and data retrieval techniques.

3. Compare and contrast approaches to the development of data intensive applications.

4. Analyse how different algorithms and data structures affect data intensive system performance.

5. Construct and apply different data representations that support data curation and analysis.

6. Design experiments for comparing and analysing different data engineering techniques.

7. Write reports that analyse properties of data engineering techniques.

Part I: Techniques for Scalability

Week 1: Storage: Storing Datasets for Scalability 
•    File Systems
•    Storage structures
•    Indexes on disk and in memory

Week 2: Algorithms
•    Algorithmic strategies
•    Modelling algorithm behaviour

Week 3: Queries
•    Query processing 
•    Modelling query properties

Week 4: Parallelism/Distribution
•    Architectures
•    Paradigms

Week 5: Platforms
•    Batch
•    Interative
•    Streaming

Week 6: 
•    Complete laboratory work.

Part I: Data Curation and Analysis

Week 7: Graph-based Data Analysis
•    Graph database
•    Graph query

Week 8: Table Representation
•    Models and learning methods
•    Discussion and applications

Week 9: Semantic Table Interpretation
•    Entity annotation
•    Type annotation
•    Attribute and relation annotation
•    Table to graph transformation

Week 10: Data Integration
•    Schema inference
•    Entity alignment

Week 11: Advanced Topics and Recent Development
•    Question answering
•    Retrieval augmented generation

Week 12: 
•    Complete laboratory work

 

The unit will adopt a blended learning approach, with videos and quizzes for students to engage with asynchronously, in addition to synchronous activities in the form of: (i) workshops that include both presentation of new material and problem solving; (ii) laboratory sessions that explore specific techniques in more detail and apply them in practice.

Summative lab-based coursework: individual rubric-based feedback after marking.
Formative weekly quizzes: Autograded quizzes providing immediate feedback.
Exam: cohort level feedback after marking.
 

Martin Kleppmann, Designing Data-Intensive Applications, O’Reilly, 2017.

Jure Leskovec, Anand Rajaraman, Jeff Ullman, Mining of Massive Datasets, 3rd Edition, Cambridge University Press, 2020.

Joe Reis and Matt Housley, Fundamentals of Data Engineering, O’Reilly, 2022.