Магистр науки по машинному обучению в науке

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.

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

• Galaxy Cluster Emulation
• Assembly of large scale structure in the Universe
• Application of ML to Fintech

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.

Modules

Core modules

• Machine Learning in Science - Part one
• Machine Learning in Science - Part two
• Machine Learning in Science - Project

Optional modules

• Big Data and Cloud Computing
• Designing Intelligent Agents
• Computer Vision
• Professional Ethics in Computing
• Introduction to Quantum Information Science
• The Physics of Deep Learning
• Neural Computation

Pathways

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.

A choice between computer science or mathematics focused ML module:

• Machine Learning
• Statistical Machine Learning

A choice between short or long statistics and probability module:

• Statistical Foundations
• Fundamentals of Statistics

A choice between long or short computing module:

• Programming
• Scientific Programming in Python

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 an 8000-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 2021 entry.

Home/ UK students

EU/ International students

English language support

If you need support to meet the required level, you may be able to attend a presessional course. Our Centre for English Language Education is accredited by the British Council for the teaching of English in the UK.

For presessional English courses, you must take IELTS for UKVI to meet visa regulations.

If you successfully complete your presessional course to the required level, you can then progress to your degree course. This means that you won't need to retake IELTS or equivalent.

Alternative qualifications

We recognise that applicants have a variety of experiences and follow different pathways to postgraduate study.

We treat all applicants with alternative qualifications on an individual basis. We may also consider relevant work experience.

If you are unsure whether your qualifications or work experience are relevant, contact us.

Where you will learn

University Park Campus

University Park Campus covers 300 acres, with green spaces, wildlife, period buildings and modern facilities. It is one of the UK's most beautiful and sustainable campuses, winning a national Green Flag award every year since 2003.

Most schools and departments are based here. You will have access to libraries, shops, cafes, the Students’ Union, sports village and a health centre.

You can walk or cycle around campus. Free hopper buses connect you to our other campuses. Nottingham city centre is 15 minutes away by public bus or tram.

Jubilee Campus

Jubilee Campus has eco-friendly and sustainable buildings, alongside green spaces, wildlife and a lake. It has won a national Green Flag award every year since 2013.

This campus is home to our business, education and computer science schools. Alongside a sports centre and student accommodation, we've developed new facilities such as the Advanced Manufacturing Building.

You can walk to University Park Campus in around 20 minutes or catch a free hopper bus. Nottingham city centre is 20 minutes away by public bus.

Careers

Careers advice

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.

More than 1,500 employers advertise graduate jobs and internships through our online vacancy service. We host regular careers fairs, including specialist fairs for different sectors.

Job prospects

Graduate destinations

Machine learning and artificial intelligence have become central to 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.

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 in 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.

The University of Nottingham was founded on the vision and philanthropic spirit of Jesse Boot who, in 1928, donated the land that is now University Park. The vision of a university devoted to the discovery, enterprise and the advancement of the human condition, combined with his lifelong commitment to improving health and wellbeing, remains intrinsic to the culture of the University today and will continue to underpin our future purpose.

We have inspiring campuses in three countries, energising us to be a globally engaged university that is also committed to making a difference in our cities and regions

We empower and support students and staff to collaborate in learning, scholarship and discovery across all realms of knowledge, solving problems and improving lives

We are stewards of a pioneering and entrepreneurial tradition of creativity and innovation