MSc Communications and Signal Processing with Extended Research

Fees for entry in 2027 have not yet been set. For reference, the fees for the academic year beginning September 2026 were as follows:

• MSc (full-time)
  UK students (per annum): £14,700 year 1, £7,350 year 2
  International, including EU, students (per annum): £38,400 year 1, £19,200 year 2

The fees quoted above are fully inclusive of tuition, administration and computational costs.

Fees for entry are subject to yearly review. The University reserves the right to increase your tuition fee by up to 7% each year for courses lasting more than one year, including to reflect rising costs associated with delivering our educational and wider student experience. Postgraduate fees information .

Always contact the admissions team if you are unsure which fee applies to your qualification award and method of attendance.

International student CAS deposit

Self-funded international applicants are required to pay a deposit of £2500 towards their tuition fees before a confirmation of acceptance for studies (CAS) is issued. Some applicants will be required to pay a higher deposit. More information on tuition fee deposits .

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.

ILO1 Design and apply machine learning and neural network algorithms or tools for regression, clustering and classification tasks in a wide range of applications.

ILO2 Design and apply algorithms for obtaining optimal solutions for convex optimization problems.

ILO3 Perform literature searching; scientific report writing; use of graphing and presentation packages; project design; team work; use of the discussion forum.

ILO4 Demonstrate a clear and detailed knowledge of the founding principles of convex sets and convex functions.

ILO5 Recognise and reason about situations arising in the use of optimization and machine learning.

ILO6 Demonstrate profound and detailed knowledge of machine learning and AI approaches to problem solving.

ILO7 Demonstrate clear knowledge of deep learning networks and their applications

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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.