MSc Communications and Signal Processing

For entry in the academic year beginning September 2026, the tuition fees are as follows:

• MSc (full-time)
  UK students (per annum): £14,700
  International, including EU, students (per annum): £38,400

Further information for EU students can be found on our dedicated EU page.

The fees quoted above will be fully inclusive for the course tuition, administration and computational costs during your studies.

All fees for entry will be subject to yearly review and incremental rises per annum are also likely over the duration of courses lasting more than a year for UK/EU students (fees are typically fixed for International students, for the course duration at the year of entry). For general fees information please visit: postgraduate fees . Always contact the department if you are unsure which fee applies to your qualification award and method of attendance.

Self-funded international applicants for this course will be required to pay a deposit of £1000 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. This deposit will only be refunded if immigration permission is refused. We will notify you about how and when to make this payment.

All students should normally be able to complete their programme of study without incurring additional study costs over and above the tuition fee for that programme. Any unavoidable additional compulsory costs totalling more than 1% of the annual home undergraduate fee per annum, regardless of whether the programme in question is undergraduate or postgraduate taught, will be made clear to you at the point of application. Further information can be found in the University's Policy on additional costs incurred by students on undergraduate and postgraduate taught programmes (PDF document, 91KB).

We offer a number of postgraduate taught scholarships and awards to outstanding UK and international students each year.

The University of Manchester is committed to widening participation in master's study, and allocates £300,000 in funding each year. Our Manchester Master's Bursaries are aimed at widening access to master's courses by removing barriers to postgraduate education for students from underrepresented groups.

We also welcome the best and brightest international students each year and reward excellence with a number of merit-based scholarships. See our range of master’s scholarships for international students .

And, if you have completed an undergraduate degree at The University of Manchester, or are currently in your final year of an undergraduate degree with us, you may be eligible for a discount of 10% on tuition fees if you choose to study on a taught postgraduate course here. Find out if you're eligible and how to apply .

For more information on master's tuition fees and studying costs, visit the University of Manchester funding for master's courses website to help you plan your finances.

1. Introduction of convex sets and convex functions.
2. Illustrate convex optimization problems, including linear programming, quadratic programming, geometric programming, semi-definite programming.
3. Introduce duality theory, including Lagrangian dual function, Lagrange dual problem, weak and strong duality, Interpretation of dual variables, KKT optimality conditions.
4. Illustrate various convex optimization methods and algorithms, such as descent methods, Newton methods, sub-gradient method, interior point method.
5. Provide some applications of convex optimization to signal processing and communications.
6. Introduction to machine learning and optimisation.
7. High-dimensional data representation. Basic multivariate statistical and regression models. Decision tree algorithms and Bayesian learning.
8. Clustering and classification algorithms including SVMs.
9. Introduction to neurons, human visual system and neural networks. Artificial neural networks (feedforward, recurrent) and their learning mechanisms: supervised and unsupervised.
10. Introduction to deep learning neural networks and their implementations.

• To provide a general overview of convex optimization theory and its applications.
•  To introduce various classical convex optimization problems and illustrate how to solve these numerically and analytically. 
• To introduce and practise basic machine learning techniques for multivariate data analysis and engineering applications. 
• To introduce and practise fundamental neural networks and their recent advances, esp. deep learning neural networks and implementations in practical applications.

• Understand the motivation and benefit of using convex optimization and machine learning.
• Establish a good understanding about convex sets and convex functions.
• Recognise typical forms of convex optimizations and their associated optimal solutions.
• Understand fundamental machine learning approaches in problem solving.
• Able to apply machine learning methods in practical data-oriented problems.
• Understand neural networks and basic deep learning networks and their applications.

• To be able to reason about situations arising in the use of optimization and machine learning.
• To be able to design algorithms for obtaining optimal solutions for convex optimization problems.
• To be able to apply problem solving approaches used in machine learning and neural networks in wider engineering tasks.
• To be able to design a machine learning or neural network algorithm or system for a given learning problem.

• To be able to apply convex optimization to practical communication systems.
• To be able to use machine learning tools or libraries in practical applications.

• Develop the capability for mathematical and algorithmic formulation.
• Develop wider problem-solving and data analytical skills in engineering.
• Scientific report writing and presentation.

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Stephen Boyd and Lieven Vandenberghe, Convex Optimization Cambridge University Press.

Chong-Yung Chi and Wei-Chiang Li , Convex Optimization for Signal Processing and Communications: From Fundamentals to Applications, CRC Press.

Richard O. Duda, Peter E. Hart, and David G. Stork, Pattern Classification, 2nd ed. Willey Interscience Publication.

Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer.

Ian Goodfellow, Toshua Bengio, and Aaron Courville, Deep Learning, MIT Press.