MSc Communications and Signal Processing

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.