Course overview

The development and use of machine learning (ML) and artificial intelligence (AI) have revolutionised areas such as computer vision, speech recognition and language processing.

On this course you will learn how to apply ML and AI techniques to real scientific problems. This will help you build vital skills, enhancing your employability in a rapidly expanding area.

Graduates of this course will learn how to:

  • identify and use relevant computational tools and programming techniques
  • apply statistical and physical principles to break down algorithms, and explain how they work
  • design strategies for applying machine learning to the analysis of scientific data sets.

In addition, you will develop a broad set of transferable skills, including communication, critical thinking, and problem-solving.

Find out what our graduates say about the course on our Physics Blog.

You will have the opportunity to develop your own research project on a topic of your choice. Previous projects have looked at:

  • Creating a Chess AI with Modern Reinforcement Learning
  • Sampling rare events with reinforcement learning
  • Tensor Network Methods for Machine Learning
  • Supervised machine learning on a quantum computer
  • Detecting dark matter substructure in galaxies
  • Characterising the continuous morphology of clusters of galaxies
  • De-'erm'ing speech
  • Identification of skin tissue structures using morphology-based AI technique
  • Predicting traffic flow using deep learning
  • Deep Learning for Drug Discovery
  • Segmentation of kidneys in MRI

Why choose this course?

Joint 3rd

in the UK for research quality

Research Excellence Framework 2014

Research project

supervised by one or more academic staff members

Learn skills

for applying machine learning and AI techniques to real scientific problems

Course content

This course consists of 180 credits, split into 120 credits of taught modules during the autumn and spring semesters, and a 60 credit research project that is completed in the summer period.


Core modules

Machine Learning in Science – Part 1

This module will provide an introduction to the main concepts and methods of machine learning. It introduces the basics of supervised, unsupervised and reinforcement learning as applied to regression, classification, density estimation, data generation, clustering and optimal control. It will be taught via two sessions per week through a combination of fundamental concepts and hands-on applications.

Machine Learning in Science – Part 2

This module will cover more advanced topics following from Machine Learning in Science Part 1, specifically the concepts and methods of modern deep learning. Topics include deep neural networks, CNNs, RNNs, GANs, RBMs and deep RBMs, autoencoders, transfer learning, reinforcement learning and Markov decision processes, cleaning data and handling large data sets. The main project for the module is the self-driving PiCar, as seen in this video.

Machine Learning in Science – Project

You will carry out a substantial investigation in the form of a research project on the application of the machine learning techniques learned as part of the course to a scientific problem.

The study will be largely self-directed, with oversight and input provided by a supervisor from the School of Physics and Astronomy, School of Computer Science or School of Mathematical Sciences. The topic will be chosen from a list of potential projects provided by the schools in the Faculty of Science. The topic could be based on a theoretical and/or computational investigation, a review of research literature, and/or a combination of the two.

Optional modules

Big Data and Cloud Computing

This module will begin by introducing a number of approaches to handling very large datasets, eg databases, indexing, chunking, parallelism, and map-reduce. You will look at some widely-used software tools which implement these methods.You will be introduced to the concept of cloud computing and give examples of the facilities provided by popular vendors. The module will also look at how big data algorithms can be implemented using cloud-based hardware, and finish with you deploying your own big data solutions in the cloud.

Designing Intelligent Agents

In this module, you will be given a basic introduction to the analysis and design of intelligent agents, software systems which perceive their environment and act in that environment in pursuit of their goals. You will cover topics including task environments, reactive, deliberative and hybrid architectures for individual agents, and architectures and coordination mechanisms for multi-agent systems.

Computer Vision 20 credits

You will examine current techniques for the extraction of useful information about a physical situation from individual and sets of images. You will learn a range of methods and applications, with particular emphasis being placed on the detection and identification of objects, image segmentation, pose estimation, recovery of three-dimensional shape and analysis of motion. These problems will be approached with both traditional and modern computer vision approaches, including deep learning.

Professional Ethics in Computing

This module looks broadly into professional ethics within the scope of the computing discipline. It covers a range of professional, ethical, social and legal issues in order to study the impact that computer systems have in society and the implications of this from the perspective of the computing profession. In particular, the module covers topics such as introduction to ethics, critical thinking, professionalism, privacy, intellectual and intangible property, cyber-behaviour, safety, reliability and accountability, all within the context of computer systems development.

Introduction to Quantum Information Science

The paradigm of Quantum Information Science (QIS) is that quantum devices, made of systems such as atoms and photons, can out-perform the present-day technology in key applications ranging from computing power and communication security to precision measurements. Quantum information processing and the measurement and control of individual quantum systems are central topics in QIS, lying at the intersection of quantum mechanics with 'classical' disciplines such as information theory, probability, and statistics, computer science and control engineering.

The aim of this module is to provide an introduction to QIS, emphasising the differences and similarities between the classical and the quantum theories. After a short review of the necessary probabilistic notions, the first part introduces the operational framework of quantum theory involving the fundamental concepts of states, measurements, quantum channels, instruments. This includes some of the influential results in the field such as entanglement and quantum teleportation, Bell's theorem and the quantum no-cloning theorem. The second part covers at least two topics from quantum Markovian evolutions, quantum statistics, continuous variable systems.

The Physics of Deep Learning

This module will teach you how to think about artificial neural networks and deep learning from the point of view of physic. It will apply concepts and methods of statistical mechanics to deep learning problems. It will show how a physical science perspective allows you to understand better the workings of neural networks to make them more efficient and expand their range of applications.

Neural Computation

The aim of this module is to teach you how neural processes can be understood in computational terms and how they can be analysed using mathematical and computational methods.

Topics included:

  • biophysical and reduced models of neurons
  • models of networks (eg Hopfield networks, ring-attractors and rate networks)
  • models of synaptic plasticity and memory
  • perceptrons
  • unsupervised learning
  • neural coding
  • visual system
  • model fitting
The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Friday 22 October 2021.

In addition, this course offers alternative strands/pathways which allow you to select different combinations of core and optional modules to meet your interests and background. You will choose one of each of the following pairs of core modules.

Choice between computer science or mathematics focused ML module:

Machine Learning 20 credits

Providing you with an introduction to machine learning, pattern recognition, and data mining techniques, this module will enable you to consider both systems which are able to develop their own rules from trial-and-error experience to solve problems, as well as systems that find patterns in data without any supervision. In the latter case, data mining techniques will make the generation of new knowledge possible, including very big data sets. This is now fashionably termed 'big data' science.

You'll cover a range of topics including:

  • machine learning foundations
  • pattern recognition foundations
  • artificial neural networks
  • deep learning
  • applications of machine learning
  • data mining techniques and evaluating hypotheses
Statistical Machine Learning 20 credits

This module is a topic at the interface between statistics and computer science, concerning models that can adapt to and make predictions based on data. This module builds on the principles of statistical inference and linear regression to introduce a variety of methods of clustering, dimension reduction, regression and classification.

Much of the focus is on the bias-variance trade-off and on methods to measure and compensate for overfitting. The learning approach is hands-on; you will be using R extensively in studying contemporary statistical machine learning methods, and in applying them to tackle challenging real-world applications.

Choice between short or long statistics and probability module:

Statistical Foundations

In this module, the fundamental principles and techniques underlying modern statistical and data analysis will be introduced. You will gain experience in using a statistical package and interpreting its output. The course will cover a 'common core' consisting of:

  • statistical concepts and methods
  • linear models
  • probability techniques
  • Markov chains
Fundamentals of Statistics

In this module you will explore the fundamental principles and techniques underlying modern statistical modelling and data analysis. The course will cover a 'common core' consisting of statistical concepts and methods, linear models, probability techniques and Markov chains. You will gain experience of using a statistical package and interpreting its output.

In addition, you will study more advanced material concerned with the two main theories of statistical inference, namely classical (frequentist) inference and Bayesian inference. Topics such as sufficiency and best-unbiased estimators are explored in detail. There is special emphasis on the exponential family of distributions. Topics in Bayesian inference include basic ingredients (prior, likelihood and posterior), conjugacy, vague peior knowledge, marginal and predictive inference, decision theory, normal inverse gamma inference, and categorical data.

Choice between long or short computing module:

Programming 20 credits

This module will give you a comprehensive overview of the principles of programming, including procedural logic, variables, flow control, input and output and the analysis and design of programs. Instruction will be provided in an object-oriented programming language.

Scientific Programming in Python

This module will introduce the Python programming language and its associated ecosystem, with a focus on the elements most applicable to scientific computing. You will begin by covering the essentials of pure Python, as well as installation, environment maintenance and version control issues. You will then introduce the 'numpy' module for efficiently dealing with array data, followed by plotting with 'matplotlib', and the various scientific tools in 'scipy7'.

The module will continue with a tour of various modules useful for different aspects of scientific programming, including data handling and analysis, symbolic mathematics, Monte Carlo sampling, tools for machine learning, and producing graphical and online interfaces.

Finally, you will cover areas of good software development, such as testing and profiling, as well as approaches for dealing with very large amounts of data or speed critical applications. 

The above is a sample of the typical modules we offer but is not intended to be construed and/or relied upon as a definitive list of the modules that will be available in any given year. Modules (including methods of assessment) may change or be updated, or modules may be cancelled, over the duration of the course due to a number of reasons such as curriculum developments or staffing changes. Please refer to the module catalogue for information on available modules. This content was last updated on Friday 22 October 2021.

Learning and assessment

How you will learn

  • Lectures
  • Problem classes
  • Workshops

There is a range of core and optional modules, as well as alternative strands, which allow you to select core and optional modules in different combinations. This allows you to choose modules to fit your undergraduate background and personal interests.

Class sizes are typically around 20-40 students. 

The course is taught by experienced academics with a track record of application of machine learning to scientific research.

How you will be assessed

  • Practical exams
  • Coursework
  • Research project
  • Project work

Modules are assessed using a variety of individual assessment types which are weighted to calculate your final mark for each module. There will be a research project assessed by a 8,000 word report.

You will need an average mark of 50% to pass the MSc overall – you won't get a qualification if you don't achieve this. You will be given a copy of our marking criteria when you start the course and will receive regular feedback from your tutors.

Research project

During the summer period, you will concentrate on an independent research project which focuses on the application of machine learning methods to original scientific problems, provided by research groups from across the Faculty of Science. The project involves writing a dissertation and is supervised by a member of the academic staff.

Contact time and study hours

On a typical week during term you will work around 30 hours: 10 contact hours and 20 hours of self-study. 

Entry requirements

All candidates are considered on an individual basis and we accept a broad range of qualifications. The entrance requirements below apply to 2022 entry.

Undergraduate degree2.1 (or international equivalent) in one of the following areas: physics, mathematics, computer science, chemistry, engineering. A 2.2 (or international equivalent) may be considered if the applicant has relevant work experience or another supporting factor.


Our step-by-step guide covers everything you need to know about applying.

How to apply


All listed fees are per year of study.

Qualification MSc
Home / UK £11,050
International £26,500

Additional information for international students

If you are a student from the EU, EEA or Switzerland, you will pay international tuition fees in most cases. If you are resident in the UK and have 'settled' or 'pre-settled' status under the EU Settlement Scheme, you will be entitled to 'home' fee status.

Irish students will be charged tuition fees at the same rate as UK students. UK nationals living in the EU, EEA and Switzerland will also continue to be eligible for ‘home’ fee status at UK universities until 31 December 2027.

For further guidance, check our information for applicants from the EU.

These fees are for full-time study. If you are studying part-time, you will be charged a proportion of this fee each year (subject to inflation).

Additional costs

We do not anticipate any extra significant costs, alongside your tuition fees and living expenses. You should be able to access most of the books you’ll need through our libraries, though you may wish to purchase your own copies which you would need to factor into your budget. Personal laptops are not compulsory as we have computer labs that are open 24 hours a day but you may want to consider one if you wish to work at home.

Due to our commitment to sustainability, we don’t print lecture notes. It costs 4p to print one black and white page. You will be given £5 worth of printer credits a year. You are welcome to buy more credits if you need them.


There are many ways to fund your postgraduate course, from scholarships to government loans.

We also offer a range of international masters scholarships for high-achieving international scholars who can put their Nottingham degree to great use in their careers.

Check our guide to find out more about funding your postgraduate degree.

Postgraduate funding


We offer individual careers support for all postgraduate students.

Expert staff can help you research career options and job vacancies, build your CV or résumé, develop your interview skills and meet employers.

Each year 1,100 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

International students who complete an eligible degree programme in the UK on a student visa can apply to stay and work in the UK after their course under the Graduate immigration route. Eligible courses at the University of Nottingham include bachelors, masters and research degrees, and PGCE courses.

Graduate destinations

Machine learning and artificial intelligence have become central for the economy and society. Graduates are highly sought after in data intensive sectors, including IT, finance, consultancy, manufacturing, as well as academic and industrial research and development. Some of our graduates go on to study at PhD level. 

Career progression

95.5% of undergraduates from the School of Physics and Astronomy secured graduate level employment or further study within 15 months of graduation. The average annual salary for these graduates was £34,063.*

* HESA Graduate Outcomes 2020. The Graduate Outcomes % is derived using The Guardian University Guide methodology. The average annual salary is based on graduates working full-time within the UK.

Two masters graduates proudly holding their certificates
" The coding skills that I developed during my MSc have proven essential in the computational projects I have undertaken, involving analytical modelling and density functional theory (DFT) simulations. I am currently a PhD student at the University of Cambridge in the department of Material Science. "
Sunny Howard

Related courses

This content was last updated on Friday 22 October 2021. Every effort has been made to ensure that this information is accurate, but changes are likely to occur given the interval between the date of publishing and course start date. It is therefore very important to check this website for any updates before you apply.