MEng Electronic Engineering

Year of entry: 2024

Course unit details:
Machine Learning & Optimisation Techniques

Course unit fact file
Unit code EEEN40151
Credit rating 15
Unit level Level 4
Teaching period(s) Semester 1
Available as a free choice unit? No


(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.


Unit title Unit code Requirement type Description
Numerical Analysis EEEN30101 Pre-Requisite Recommended


The unit aims to:

(1) To provide a general overview of convex optimization theory and its applications.

(2) To introduce various classical convex optimization problems and illustrate how to solve these numerically and analytically.  

(3) To introduce and practise basic machine learning techniques for multivariate data analysis and engineering applications.

(4)  To introduce and practise fundamental neural networks and their recent advances, esp. deep learning neural networks and implementations in practical applications.

Learning outcomes

  • 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

Knowledge and understanding



Intellectual skills

  • 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

Practical skills

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

Transferable skills and personal qualities

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

Assessment methods

Method Weight
Other 30%
Written exam 70%


Duration: 3 hours. (70%)


Machine Learning Coursework (15%)

Optimisation Techniques (15%)

Feedback methods


Study hours

Scheduled activity hours
Lectures 27
Practical classes & workshops 18
Tutorials 6
Independent study hours
Independent study 99

Teaching staff

Staff member Role
Hujun Yin Unit coordinator
Khairi Hamdi Unit coordinator

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