- Start date: Mid-September
- Study mode and duration: 12 months full-time
Ranking: Number 5 in the UK for Electronic & Electrical Engineering by Complete University Guide 2021
Study with us
- 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
- industry-engaged research projects
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.
Delivered jointly by the Departments of Electronic & Electrical Engineering and 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 industrial internships
You'll also have the opportunity to complete the project through our competitive MSc industrial internships. 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.
Interaction with industry is provided through our internships, 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.
The Departments have excellent teaching facilities including interactive classrooms and state-of-the-art laboratories with the latest computing equipment. You'll have the opportunity to engage with academics and researchers in our Machine Learning and Neuromorphic Technology Unit.
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.
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)
The aim of the class is 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, to enable students to write programs which can execute in massively parallel cloud-based infrastructures
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.
One to be chosen.
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.
Embedded Systems Design
This class provides hands-on experience in translating Digital Signal Processing concepts into real-time embedded systems applications.
Through a combination of lectures, up-to-date technical discussions and hardware programming, you'll learn to design and implement real-time embedded systems through familiarisation with Digital Signal Processors and FPGAs.
Advanced Topics in Software Engineering (20)
This class aims to:
- make students aware of key aspects of current software engineering research
- familiarise students with the state-of-the-art in terms of what problems can be solved and what are the current exciting challenges
- develop the necessary skills in students to allow them to contribute to the software engineering research community
- equip students with the skills and background to appreciate the contributions to software engineering research across the full range of material presented at the key international conferences in the field
Information Access & Mining
MSc Project (Industrially engaged)
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 number of industrial internship projects are available.
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.
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 and Conduct.
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.
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 EU/UK) who do not meet the academic entry requirements for a Masters degree at University of Strathclyde. The Pre-Masters programme provides progression to a number of degree options.
Upon successful completion, you will be able to progress to this degree course at the University of Strathclyde.
We've a thriving international community with students coming here to study from over 100 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
Fees & funding
All fees quoted are for full-time courses and per academic year unless stated otherwise.
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Course materials & costs
The 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 trips
The 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.
Students are not required to purchase any specific software licenses – all software used is available on campus machines, either locally or remotely.
All undergraduates and PGI 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.
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.
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 and non-UK EU postgraduate students
Scottish and non-UK EU 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.
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.
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.
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.
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.
Data Lab Scholarship
The Data Lab is a collaborative programme between The Data Lab and eleven Scottish universities, including Strathclyde. It facilitates industry involvement and collaboration, and provides full funding and resources for students. A number of scholarships are available for Scottish/EU students studying this course. Find out more.
Students who wish to be considered for these scholarships need to ensure that they fulfil the eligibility criteria set out by the Scottish Funding Council (SFC).
Applicants do not need to submit a separate application form to be considered for one of the Data Lab Scholarships. The Departments Admissions Panel will review all applications submitted by the deadline date of 31 July, with all eligible applicants automatically considered for these scholarships.
The scholarships will be awarded on a competitive basis, taking into consideration academic performance, references and any other relevant information. Applicants should ensure that all information they wish to be considered by the Panel, has been included in their MSc application. Successful candidates will be advised of their scholarship award by 12 August.
Faculty of Engineering Excellence Scholarship (FEES) for International Students
If you're applying for an MSc course you'll be eligible to apply for a Faculty of Engineering Excellence Scholarship offering up to £3,000 towards your tuition fees.
The scholarship is available for application to all self-funded, new international (non-EU) fee paying students holding an offer of study for an MSc programme 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 MSc programme at Strathclyde in the coming academic year (2019-20).Find out more
Job titles for future graduates of th 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
Glasgow is Scotland's biggest & most cosmopolitan city
Our campus is based in the very heart of Glasgow, Scotland's largest city. National Geographic named Glasgow as one of its 'Best of the World' destinations, while Rough Guide readers have voted Glasgow the world’s friendliest city! And Time Out named Glasgow in the top ten best cities in the world - we couldn't agree more!
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.
Find out what some of our students think about studying in Glasgow!Find out all about life in Glasgow
Machine Learning and Deep Learning
Start Date: Sep 2020
Mode of Delivery: full-time
Machine Learning and Deep Learning - UESTC
Start Date: Sep 2020
Mode of Delivery: full-time
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