Studying machine learning

Develop an in-depth understanding of machine learning models and learn to apply them to real-world problems.

Key information

Duration: 2 years part-time
Start date: October 2021
Location: Online
ECTS: 90 credits

Applications will open on 8 January 2021


This Master's course aims to accelerate your career in engineering or data science, enabling you to choose a path that’s right for you. This could be as a data scientist, a machine learning engineer, or a computational statistician.

This is an online and part-time course. This gives you the chance to participate even if you can't study in London or full-time.

With hands-on projects, you’ll build a portfolio to showcase your new skills. Everything from probabilistic modelling and deep learning to unstructured data processing and anomaly detection.

Through foundations in mathematics and statistics, you will be able to boost your confidence in analytical skills.

You will have the chance to gain expertise in implementing scalable solutions using industry-standard tools, including PySpark. This gives you the tools to tackle big and complex data.

Curriculum covering the ethics and limitations of machine learning will enable you to ethically apply these techniques to your work.

When you graduate, will have the ability to:

  • Turn data into actionable insights
  • Contribute to strategic decision making
  • Become a responsible member of this growing profession

Online study

This is a fully online Master's degree, delivered through the Coursera platform.

The course and online platform are designed to give you a seamless, flexible and engaging learning experience.

Learn through a range of online methods, including:

  • Lectures
  • Tests
  • Tutorials
  • Coding exercises

With your cohort, you will participate in discussion boards and graded discussion prompts. To work effectively, you will be given core reading and develop your critical thinking and transferrable skills.

Online features include:

  • Live classroom sessions
  • Global team projects
  • Targeted academic feedback

Study programme

Throughout the course, you will have the opportunity to directly engage with departmental faculty at Imperial and learn with guided applications to real-world problems.

Across both years, you will learn through a programme of core modules. These cover topics from Deep Learning to Big Data and Data Science.

Your development culminates in a research project in Summer term of your final year.


This course prepares you for advanced engineering roles in areas such as AI, data science and machine learning.

Future roles could include:

  • Data scientist
  • Machine learning engineer
  • Computational statistician


Modules shown are for the current academic year and are subject to change depending on your year of entry.

There are limited circumstances in which we may need to make changes to or in relation to our courses. See what changes we may make and how we will tell you about them.

Find out more about potential changes



You will study all of the modules below. This includes a substantial research project in your final year.

Year one

Applicable Maths

This module will provide the statistical and mathematical tools used in the later modules. This module will review basic probability and differentiation and integration and cover, in more detail, eigenvalue decomposition, optimisation techniques and modes of convergence.

Ethics in Data Science and Artificial Intelligence (Part 1-3) Part 1

This module will investigate the ethical implications of the new capabilities offered by Data Science and Artificial Intelligence. It will begin by discussing the ethical use of data itself - the raw materials of data science pipelines. It will then discuss sets of principles that tech leaders and international bodies are adopting to promote ethical use of data science and artificial intelligence algorithms, including a discussion of real-world examples of failings and adverse outcomes. Finally, it will investigate ways in which data science and artificial intelligence can usher in novel solutions to moral problems of old, such as prejudice and bias, or even usher in entirely novel moral dilemmas.

Programming for Data Science

This module will provide the skills and knowledge to support the implementation, test and deployment of Machine Learning algorithms, and to construct data processing and analytic pipelines for Data Science. The module will compare and contrast R and Python, the two most popular Data Science languages, and students will become fluent using both languages for the range of challenges arising in practical data analysis problems.

Exploratory Data Analytics and Visualisation

This module will provide the skills and knowledge required to produce convincing narrative summaries and informative visualisations for a variety of complex datasets. The module covers assessing the structure and evaluating the quality of data and outlines techniques which uncover the underlying structure in data, both for initial reporting to a variety of intended audiences and to provide guidance for potential formal analysis and model formulation.  The analysis and visualisations will be predominantly implemented using the suite of R tidyverse packages.

Supervised Learning

This module will introduce the framework of supervised learning. Students will learn the framework of linear models and see examples of their extension to generalized linear models. General principles of modelling will be discussed, and students will learn the models of failure encountered when working with flexible, non-parametric models. A number of modern non-parametric methods for regression and classification will be considered, and their performance evaluated on datasets from a variety of scientific problem domains. The emphasis throughout will be on principled, uncertainty-aware modelling.

Ethics in Data Science and Artificial Intelligence (Part 1-3) Part 2

This is the second part of this module.

Big Data: Statistical scalability with PySpark

This specialisation consists of three components – distributed programming, Spark, statistical analysis at scale. Big Data technology is often seen as a panacea to all data processing problems. In this topic, the students will explore and establish when it is appropriate to utilise Big Data technology in order to perform data analysis. The students will learn statistical concepts such as Bayesian parameter estimation with large scale data and will explore data sampling strategies in a Big Data world.

Bayesian Methods

This specialisation introduces students to subjective probabilities and the Bayesian paradigm for making coherent individual decisions in the presence of uncertainty.  The course will blend classical fundamental principles and mathematical rigour with a modern, high-level overview of a broad range of modern statistical techniques. Computer software packages will be introduced for implementing specific inferential procedures required for sophisticated Bayesian analyses.

Year two

Deep Learning

This module teaches the building blocks of deep learning models, and how to design network architectures for specific applications, in both supervised and unsupervised contexts. It covers practical skills in implementing neural networks in the popular deep learning library PyTorch. Students will learn how to train and evaluate networks using this framework. A central focus of the module is on the mathematical and statistical foundations of some of the most sophisticated deep learning models, such as generative adversarial networks (GANs), variational autoencoders (VAEs) and Bayesian methods for neural networks.

Unsupervised Learning

This module will provide an in-depth introduction to the different challenges of unsupervised learning. Topics will include clustering, factor analysis, dimensionality reduction, anomaly, outlier and change point detection.

Ethics in Data Science and Artificial Intelligence (Part 1-3) Part 3

This is the third and final part of this module.

Unstructured Data Analysis

This module provides you with skills and knowledge to handle "unstructured' data, such as images, text and network data. Data science is replete with problems that involve unstructured data and this module develops methods for converting unstructured data to a more familiar "structured" form for use with standard Machine Learning methods as well as direct approaches with unstructured data. Examples will include natural language processing and network analysis.

Learning Agents

In an automated machine learning process, algorithms that make both inference and select decisions might be called learning agents. This module develops the expertise for taking machine learning beyond prediction process to formal decision-making processes. By contrasting issues that arise in the study of randomized controlled trials and formally designed experiments with issues related to the observational data, the module develops the theory and methodology of decision making through the theory of optimal decisions.

Research Project

This substantial research project gives you the opportunity to demonstrate what you have learned over the programme. 

Note: Ethics in Data Science and Artificial Intelligence (Part 1-3) is one 7.5 ECTS module

Research project

Your final research project provides training in research focused on machine learning and data science.

The module provides training in research on open-ended problems and gives you the opportunity to demonstrate the synthesis of the material taught over the programme. Research projects may be theoretical, methodological or applied depending on your interests.

The project is structured like a typical research study:

  • Literature review
  • Underpinning learning or exploratory data analysis
  • Study design
  • Project proposal
  • Final deliverable

Each stage is supported by a summative assessment, providing the opportunity for both feedback and direction on following stages. The final assessment involves both a written report and an oral examination. In both cases, consideration will be given to communication with a technical audience, and a lay audience.

At the end of the project, you will have a comprehensive research project that will equip you with the skills, knowledge and expertise to pursue research in Machine Learning and Data Science.

Teaching and assessment

Teaching methods

As a fully online degree, the programme will be delivered through the Coursera platform, structured through modules.

The platform allows you to have a seamless, innovative and differentiated learning experience through:

  • recorded lectures;
  • online tests;
  • scheduled live tutorials;
  • coding exercises;
  • live classroom sessions;
  • assessments and targeted academic feedback;
  • global collaborations with other students through applied projects;
  • participation in a vibrant and supportive social learning community.

You will learn as a cohort through discussion boards and peer-assessed exercises.

Other learning areas may include:

  • Core reading
  • Critical thinking skills
  • Transferrable skills

Assessment methods

The format of assessments will vary according to the aims, content and learning outcomes of each module. However, most modules are assessed primarily by coursework. Varied assessments will allow a full evaluation of your learning and achievements.

For each module, there will be short summative and formative assessments. These are typically followed by a substantive final summative assessment. This structure allows you to improve through the duration of the programme.

Through your final research project, each stage is supported by a summative assessment. This gives you the opportunity for feedback and direction for the following stages.

Further assessment methods may include:

  • Online quizzes
  • Multiple choice question exams

Overall Workload

Your overall workload consists of lectures sessions and independent learning. While your actual contact hours may vary, the following gives an indication of how much time you will need to allocate to different activities at each level of the programme.

 Year 1Year 2Total
Lectures and tutorials 235 hours (22%) 165 hours (15%)  2,190 hours
Independent study 830 hours (78%) 960 hours (85%)
Based on the typical pathway through the course

Each ECTS credit taken equates to an expected total study time of 25 hours.

Entry requirements

We welcome students from all over the world and consider all applicants on an individual basis.

Entry requirements

Minimum academic requirement

Our minimum requirement is a 2.1 degree in mathematics, applied mathematics, engineering or physics.

Alternatively, a professional or other qualification obtained by written examinations may be accepted if approved by the College.

International qualifications

We also accept a wide variety of international qualifications.

The academic requirement above is for applicants who hold or who are working towards a UK qualification.

For guidance see our Country Index though please note that the standards listed here are the minimum for entry to the College, and not specifically this Department.

If you have any questions about admissions and the standard required for the qualification you hold or are currently studying then please contact the relevant admissions team.

English language requirement (all applicants)

All candidates must demonstrate a minimum level of English language proficiency for admission to the College.

For admission to this course, you must achieve the higher College requirement in the appropriate English language qualification. For details of the minimum grades required to achieve this requirement, please see the English language requirements for postgraduate applicants.

Dual enrolment

You cannot register/enrol for more than one award at the same time. This includes awards at Imperial and other universities or institutions. You would need to de-register from your current course before starting. Read more about this in the Imperial College General Academic Regulations (Section 5.5).

Competence standards

Competence standards for our courses are used to determine whether or not a student has a specific level of competence or ability. These standards can be found Competence Standards for all MSc Maths Degree Programmes‌.

Imperial College London believes in providing the widest practicable access to all our degree programmes and will make reasonable adjustments for students wherever possible. If you do not meet one or more of the competence standards above, please submit your application and you will be able to discuss your situation with the College’s education office.


How to apply

How to apply

Important information for applicants fom Iran, Sudan, Crimea, Cuba, Syria and North Korea

This programme is delivered fully online via our platform partner, Coursera. United States export control regulations prevent Coursera from offering services and content to users in certain countries or regions. More information about which countries or regions are affected can be found on Coursera’s website.

Coursera must enforce these restrictions in order to remain in compliance with US law and, for that reason, we advise that all interested applicants check this information before applying to the programme. As a result, we are not able to consider applications for the programme for those who wish to study the programme from within these countries.

If any interested applicants have any queries regarding the above, please contact:

Application process

Applications will open on 8 January 2021

The application process takes around 10 to 12 weeks. We will not make any offers before April 2021.

Making an application

Apply online

All applicants must apply online.

Visit our Admissions website for details on the application process.

You can submit one application form per year of entry. You can usually choose up to two courses.

Application fee

For 2021–22 entry, we are introducing a pilot application fee for our Master's courses. This helps cover some of the administrative and staffing costs associated with processing the large volume of applications we receive.

This fee will be £80 per application and not per course. 

We will waive the fee for any applicant – Home or Overseas – who demonstrates that they are experiencing financial hardship.

There is no application fee for Postgraduate Certificates, Postgraduate Diplomas or PhDs. The fee for MBA applications to the Imperial College Business School is £135. 

Find out more about the application fee and waiver

ATAS certificate

An ATAS certificate is not required for overseas students applying for this course.

Tuition fees and funding

The level of tuition fees you pay is based on your fee status, which we assess based on UK government legislation.

For more information on the funding opportunities that are available, please visit our Fees and Funding website.

Tuition fees

Home rate of tuition

2021 entry

Part-time - £14,500 per year

Fees are charged by year of entry to the College and not year of study.

Except where otherwise indicated, the fees for students on courses lasting more than one year will increase annually by an amount linked to inflation, including for part-time students on modular programmes. The measure of inflation used will be the Retail Price Index (RPI) value in the April of the calendar year in which the academic session starts e.g. the RPI value in April 2021 will apply to fees for the academic year 2021–2022.

Overseas rate of tuition

2021 entry

Part-time - £14,500 per year

Fees are charged by year of entry to the College and not year of study.

Except where otherwise indicated, the fees for students on courses lasting more than one year will increase annually by an amount linked to inflation, including for part-time students on modular programmes. The measure of inflation used will be the Retail Price Index (RPI) value in the April of the calendar year in which the academic session starts e.g. the RPI value in April 2021 will apply to fees for the academic year 2021–2022.

Postgraduate Master's loan

If you're a UK national, or EU national with settled or pre-settled status under the EU Settlement Scheme, you may be able to apply for a Postgraduate Master’s Loan from the UK government, if you meet certain criteria.

For 2020-21 entry, the maximum amount was of £11,222. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.


We offer a range of scholarships for postgraduate students to support you through your studies. Find out more about our scholarships to see what you might be eligible for.

There are a number of external organisations also offer awards for Imperial students, find out more about non-Imperial scholarships.

Accommodation and living costs

Living costs, including accommodation, are not included in your tuition fees.

You can compare costs across our different accommodation options on our Accommodation website.

A rough guide to what you might expect to spend to live in reasonable comfort in London is available on our Fees and Funding website.

Further information

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