MSc Machine Learning & Deep Learning
ApplyKey facts
- Start date: September & January
- Study mode and duration: 12 months full-time
Study with us
Studying an MSc in Machine Learning & Deep Learning at the University of Strathclyde, you'll be learning at an award-winning academic institution - the only University to have won the Times Higher Education University of the Year award twice (2012/2019).
- develop expert knowledge of, and the ability to design, complex machine learning and deep neural networks systems for use in industry
- focus on architectures, algorithms and novel engineering and software technologies
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Why this course?
Machine learning and deep neural network systems are currently used by leading organisations worldwide and research centres in a wide range of applications and products. This course is for engineers and scientists looking to gain the necessary skills to be able to design these systems for use in industry.
Our MSc Machine Learning & Deep Learning degree focuses on state-of-the-art technologies for machine learning and deep neural network systems. The emphasis is on architectures, algorithms and implementation with applications in a diverse range of areas.
Administered by the Department of Electronic & Electrical Engineering, and jointly delivered with the Department of Computer & Information Sciences, you'll be exposed to state-of-the-art engineering and software technologies that underpin machine learning and deep neural network systems.
You'll learn about and gain experience from hands-on, industry relevant projects and examples. This includes programming languages and engineering tools used in an increasing number of products and services worldwide.
What you'll study
You'll complete six classes over two semesters comprising compulsory and elective taught classes. These are followed by a three-month research project in a chosen area.
If you've previously taken a similar class to one of the compulsory classes, you'll be offered an alternative.
MSc industrially engaged projects
You'll also have the opportunity to complete the MSc project through our competitive industrially engaged projects. These are offered in collaboration with selected industry partners.
You'll address real-world engineering challenges the partners are facing, with site visits, access, and provision of relevant technical data and/or facilities provided. You'll also have an industry mentor and an academic supervisor.
Industry engagement
Interaction with industry is provided through our industrially engaged projects, teaching seminars and networking events. Companies such as Leonardo, Comcast (Sky), ORE Catapult, PNDC, Xilinx, Texas Instruments, MathWorks, NHS, Canon Medical Research and Varian Ltd are just a few examples of the industry partners you can engage with during your course.
Facilities
The Departments have excellent teaching facilities including interactive classrooms and state-of-the-art laboratories with the latest computing equipment. You'll also have access to our IT facilities, including web-based resources, wireless internet and free email. An IT support team is available to help with all your needs.
Compulsory
Autonomous Sensing, Reasoning & Deep Learning
This module aims to provide background education and experience in machine intelligence and autonomous system design from the algorithm level. Students will learn the basics of the predominant data analysis, machine learning, and decision-making algorithms in use today as well as applying their knowledge to a set of simple automation tasks for both real and simulated platforms in the laboratory and on their own computers.
Digital Signal Processing Principles
This class covers the fundamentals of discrete time convolution, correlation, transform methods, time frequency signal representation, downsampling/upsampling and digital filters that are core to state of the art machine learning and deep learning architectures. The class has an integral Matlab based laboratory set of tasks that students are required to undertake.
Big Data Technologies
20 credits
In this module you will learn to:
- understand the fundamentals of Python to enable the use of various big data technologies
- understand how classical statistical techniques are applied in modern data analysis
- understand the potential application of data analysis tools for various problems and appreciate their limitations
- be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing design trade-offs and their impacts
- be familiar with the Map-Reduce programming paradigm
Machine Learning for Data Analytics
The aim of the class is to:
- understand the aims and fundamental principles of machine learning
- understand a range of the key algorithms and approaches to machine learning
- be able to apply the algorithms covered and interpret the outcomes
- understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations
Assignment & Professional Studies
The aim of this class is to provide you with support for your general academic and professional development.
You'll undertake an advanced investigation of an electronic or electrical engineering topic of your choice, to enhance your learning, and develop presentation and communication skills.
Elective
Image & Video Processing
This class will provide an introduction to the techniques relevant to digital images and video. This includes techniques both to process images and video and also to efficiently compress and communicate them.
The class will give you a comprehensive understanding of various image and video processing and coding standards. You'll also study some key applications of these standards.
Information Access & Mining (20 credits)
This module will allow you to understand the fundamentals of information access and information mining. The module will cover a range of techniques for extracting information from textual and non-textual resources, modelling the information content of resources, detecting patterns within information resources and making use of these patterns.
Project
MSc Project
The aim of the research project is to provide you with an opportunity to bring your knowledge and skills together and deploy them in a significant practical investigation, using relevant engineering literature, and where relevant, initial experiments or simulations. A limited number of industrially engaged projects are available.
Compulsory
All of the following classes are compulsory as part of the January intake to this programme.
Autonomous Sensing, Reasoning & Deep Learning
This module aims to provide background education and experience in machine intelligence and autonomous system design from the algorithm level. Students will learn the basics of the predominant data analysis, machine learning, and decision-making algorithms in use today as well as applying their knowledge to a set of simple automation tasks for both real and simulated platforms in the laboratory and on their own computers.
Digital Signal Processing Principles
This class covers the fundamentals of discrete time convolution, correlation, transform methods, time frequency signal representation, downsampling/upsampling and digital filters that are core to state of the art machine learning and deep learning architectures. The class has an integral Matlab based laboratory set of tasks that students are required to undertake.
Machine Learning for Data Analytics
The aim of the class is to:
- understand the aims and fundamental principles of machine learning
- understand a range of the key algorithms and approaches to machine learning
- be able to apply the algorithms covered and interpret the outcomes
- understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations
Assignment & Professional Studies
The aim of this class is to provide you with support for your general academic and professional development.
You'll undertake an advanced investigation of an electronic or electrical engineering topic of your choice, to enhance your learning, and develop presentation and communication skills.
Reasoning for Intelligent Agents
By the end of the module you should understand:
- classifications of problem features for autonomous systems
- common approaches to deliberate reasoning for intelligent agents
- the challenges in integrated planning and execution, and some approaches to solving them
- how to build systems in the Robot Operating System
Deep Learning Theory and Practice
By the end of the module you should be able to:
- understand the challenges in the training of deep neural network and how to overcome the challenges with few shot learning techniques
- understand how to process sequential data with deep neural network and the applications in natural language processing
- develop understanding of the potentials and generalisation of deep neural networks through theoretical analysis
- understand deep generative models and their main approaches
- understand how to build Deep Neural Networks in Python using packages such as Keras/PyTorch and implement such networks
Project
MSc Project
The aim of the research project is to provide you with an opportunity to bring your knowledge and skills together and deploy them in a significant practical investigation, using relevant engineering literature, and where relevant, initial experiments or simulations. A limited number of industrially engaged projects are available.
A supplementary (non-credit bearing) Python training module will be made available on MyPlace for January start students – this is very strongly recommended for those students that have no previous experience of the software before starting the course.
Students progressing from University strategic partner Silesian University of Technology (SUT) will complete the MSc research project and the following three classes*:
- Autonomous Sensing, Learning, and Reasoning
- Digital Signal Processing Principles
- Machine Learning for Data Analytics
*For class descriptors please see the previous tabs.
Learning & teaching
Our teaching and learning methods ensure you'll develop not only technical engineering expertise but also communications, project management and leadership skills.
Teaching and learning methods include interactive lectures, problem-solving tutorials and practical project-based laboratories. Our technical and experimental officers are available to provide support and guidance.
For each module, you'll have approximately five hours of direct teaching per week. In addition, you're expected to undertake a further five to six hours of self-study, using our web-based virtual learning environment (Myplace), research journals and library facilities.
In some classes, you'll undertake group projects. These will help to develop your interpersonal, communication and transferable skills essential to a career in industry.
Assessment
Each module has a combination of written assignments, individual and group reports, oral presentations, practical lab work and, where appropriate, an end-of-term exam.
Assessment of the summer research project consists of four elements, with individual criteria:
- interim report
- poster/demonstration presentation
- final report
- conduct
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Our Unibuddy ambassadors can answer all the questions you might have about courses and studying at Strathclyde, along with offering insight into their experiences of life in Glasgow and Scotland.
Chat now!Entry requirements
| Academic requirements | Normally a first-class or second-class honours degree (or international equivalent) in electronic or electrical engineering, or computer science. Highly-qualified candidates from other relevant engineering or science-related disciplines may be considered. |
|---|---|
| English language requirements | If English is not your first language, please visit our English language requirements page for full details of the requirements in place before making your application. |
Pre-Masters preparation course
The Pre-Masters Programme is a preparation course held at the University of Strathclyde International Study Centre, for international students (non-UK/Ireland) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde.
Upon successful completion, you'll be able to progress to this degree course at the University of Strathclyde.
Please note: Previous Maths & English qualifications and your undergraduate degree must meet GTCS minimum entry requirements as well as the pre-Masters course and an interview will be conducted before an offer can be made.
International students
We've a thriving international community with students coming here to study from over 140 countries across the world. Find out all you need to know about studying in Glasgow at Strathclyde and hear from students about their experiences.
Visit our international students' section
In a nutshell, I like that there's a lot of practical and applicable learning here. The environment is friendly, supportive, diverse and inclusive.
Glasgow is Scotland's biggest & most cosmopolitan city
Our campus is based right in the very heart of Glasgow. We're in the city centre, next to the Merchant City, both of which are great locations for sightseeing, shopping and socialising alongside your studies.
Life in Glasgow
Fees & funding
All fees quoted are for full-time courses and per academic year unless stated otherwise.
Please note, for courses that have a January 2024 start date, 2023/24 academic year fees will apply. For courses that have a September 2024 start date, 2024/25 academic year fees will apply.
Fees may be subject to updates to maintain accuracy. Tuition fees will be notified in your offer letter.
All fees are in £ sterling, unless otherwise stated, and may be subject to revision.
Annual revision of fees
Students on programmes of study of more than one year (or studying standalone modules) should be aware that tuition fees are revised annually and may increase in subsequent years of study. Annual increases will generally reflect UK inflation rates and increases to programme delivery costs.
| Scotland | £10,800 |
|---|---|
| England, Wales & Northern Ireland | £10,800 |
| International | £28,250 |
| Additional costs | Course materials & costsThe department provides a service whereby printed notes are available to the students subject to a small charge to cover copying costs. Students are recommended/required to have copies of such notes but we provide access to both printed copies and e-copies. The latter are provided without charge – in accordance with University policy. Any printed material that is mandatory (in that form) is provided with no additional charge to the students. Expect that students pay around £100 for additional course materials and books. Placements & field tripsThe department and student societies support a number of industrial visits throughout the year. These trips are not mandatory for specific programmes and modules and any incurred charge to cover transport is either met by the students or by the department. Other costsStudents are not required to purchase any specific software licenses – all software used is available on campus machines, either locally or remotely. All students are provided for the duration of their course with student-membership of IET (Professional Body) paid for by the department. Some hardware (micro controllers, design boards) may be made available to students for loan subject to appropriate refundable deposit. Students may consider purchase of low cost microcontroller boards for project work - cost from £10-£30. Access to EEE Computer labs out of working hours is via card access - card cost is £20 - refundable on return of card. Visa and immigrationInternational students may have associated visa and immigration costs. Please see student visa guidance for more information. Students are provided with an additional print-quota for use in EEE labs for EEE classes conducted in EEE computer labs. Paid top-ups possible via University IT services. Expected printing and report binding costs are around £10-£15 a year - will depend upon exact programme and class assignments. Binding is provided at cost (50p to £1.00) by EEE Resource Centre in R4.01. |
| Available scholarships | Take a look at our scholarships search for funding opportunities. |
Please note: the fees shown are annual and may be subject to an increase each year. Find out more about fees.
How can I fund my course?
Scottish postgraduate students
Scottish postgraduate students may be able to apply for support from the Student Awards Agency Scotland (SAAS). The support is in the form of a tuition fee loan and for eligible students, a living cost loan. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from England
Students ordinarily resident in England may be to apply for postgraduate support from Student Finance England. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from Wales
Students ordinarily resident in Wales may be to apply for postgraduate support from Student Finance Wales. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
Students coming from Northern Ireland
Postgraduate students who are ordinarily resident in Northern Ireland may be able to apply for support from Student Finance Northern Ireland. The support is a tuition fee loan of up to £5,500. Find out more about the support and how to apply.
Don’t forget to check our scholarship search for more help with fees and funding.
International students
We've a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.
Faculty of Engineering International Scholarships
If you're an international applicant applying for a full-time, on-campus postgraduate taught course in the Faculty of Engineering, you'll be eligible to apply for a scholarship award equivalent to a 15% reduction of your fees, which will typically be up to £4,240. In addition to this, we also have a limited number of Dean’s International Excellence Awards for our postgraduate taught applicants. These scholarships are worth £5,000 and £8,000 and will be offered to exceptional applicants at postgraduate taught level only. Applicants need to only submit one application and will be considered for all levels of postgraduate taught scholarships.
Scholarships are available for applicants to all self-funded, new international (non-EU) fee-paying students holding an offer of study for a full-time, on-campus postgraduate taught course in the Faculty of Engineering at the University of Strathclyde.
Please note you must have an offer of study for a full-time course at Strathclyde before applying. You must start your full-time postgraduate taught course at Strathclyde in the coming academic year (2024-25), this can be in September 2024 or January 2025.
The deadline for applications for the Dean’s International Excellence Award is 28 June 2024.
Faculty of Engineering Scholarships for International StudentsCareers
Job titles for future graduates of the MSc Machine Learning & Deep Learning include (but not limited to):
- Graduate Software Engineer
- Electronic Engineering Systems Analysts
- Lecturer / Researcher
- Data Scientist
- Data Engineer
- Data Analyst
- Machine Learning Engineer
- Data Insight Analyst
Apply
During the application process, you're required to upload the following supporting documents. If these are not provided, we'll not be able to process your application:
- certified individual semester mark sheets/academic transcript showing subjects taken and grades achieved for all qualifications
- if still studying, provide individual semester mark sheets to date
- provide evidence of strong mathematical skills, and a basic understanding and knowledge of Fourier Analysis and Signals and Systems
- demonstrate fundamental knowledge and practical experience of computer programming, providing examples of types of languages used and in what capacity
- certified degree certificate for all qualifications
- if still studying, provide this after completing the qualification
- provide evidence of suitable English language proficiency if English is not your first language, or you're not from a “UKVI recognised "Majority English Speaking" country”; check the University’s language requirements
- if you have been out of full-time education for over two years, provide a CV, detailing employment history, organisations worked for and a brief description of roles and responsibilities
- a copy of your passport containing your photo and passport number
- a copy of your sponsor letter/scholarship award (if appropriate)
- names, job titles and email addresses for two nominated referees
Start date: Sep 2024
Machine Learning and Deep Learning