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MScMachine Learning & Deep Learning

Why this course?

Data Lab scholarships available for Scottish/EU students

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.

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.

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.

Your study

You'll complete two semesters of compulsory and elective taught classes. These are followed by a three-month research project in a chosen area.

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.

Industry engagement

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.

Facilities

The Departments have a wide range of excellent teaching facilities available. These include interactive classrooms and brand new state-of-the-art laboratories equipped with the latest computing equipment. You'll have the opportunity to engage with academics and researchers in the Machine Learning and Neuromorphic Technology Unit in the University.

You'll also have access to our IT facilities, including web-based resources, wireless internet, and free email.

There's an IT support team to help with all your needs.

Course content

On our Masters in Machine Learning & Deep Learning you're required to take 100 credits of compulsory modules and at least 20 credits of elective modules. You're also required to complete an MSc Project.

If you've previously taken a similar class in one of the compulsory classes, you'll be offered an alternative class.

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

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.

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.

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

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
This class will allow you to understand the fundamentals of information access and information mining. The class 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.

May to September

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

A blend of teaching and learning methods including interactive lectures, problem-solving tutorials, and practical project-based laboratories will be used. Our technical and experimental officers are available to support and guide you on the individual subject material.

Each module comprises approximately five hours of direct teaching per week. To enhance your understanding of the technical and theoretical topics covered in these, you are 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.

The teaching and learning methods used to ensure you'll develop not only technical engineering expertise but also communications, project management, and leadership skills.

You will undertake group projects in some of the classes. These will help to develop your interpersonal, communication and transferable skills essential to a career in industry.

Assessment

A variety of assessment techniques are used throughout the course. You'll complete at least six modules. 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.

Entry requirements

You'll need a first or good second-class UK Honours degree, or equivalent overseas qualification, in electronic or electrical engineering, or computer science, from a recognised academic institution.

Highly qualified candidates from other relevant engineering or science-related disciplines may be considered.

Candidates whose first language is not English or who have not undertaken their undergraduate course in the UK must possess a recent UKVI-recognised English qualification.

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.

Fees & funding

2019/20

All fees quoted are for full-time courses and per academic year unless stated otherwise.

Scotland/EU

  • £8,100

Rest of UK

  • £9,250

International

  • £20,050

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).

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.

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 have a large range of scholarships available to help you fund your studies. Check our scholarship search for more help with fees and funding.

Please note

The fees shown are annual and may be subject to an increase each year. Find out more about fees.

Careers

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

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Machine Learning and Deep Learning

Qualification: MSc, Start date: Sep 2019, Mode of delivery: attendance, full-time

Machine Learning and Deep Learning - UESTC

Qualification: MSc, Start date: Sep 2019, Mode of delivery: attendance, full-time

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