- UCAS course code
- H601
- UCAS institution code
- M20
Master of Engineering (MEng)
MEng Electrical and Electronic Engineering with Industrial Experience
*This course is now closed for applications for 2025 entry.
- Typical A-level offer: AAA including specific subjects
- Typical contextual A-level offer: AAB including specific subjects
- Refugee/care-experienced offer: ABB including specific subjects
- Typical International Baccalaureate offer: 36 points overall with 6,6,6 at HL, including specific requirements
Course unit details:
Machine Learning & Optimisation Techniques
Unit code | EEEN40151 |
---|---|
Credit rating | 15 |
Unit level | Level 4 |
Teaching period(s) | Semester 1 |
Available as a free choice unit? | No |
Overview
(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.
Pre/co-requisites
Unit title | Unit code | Requirement type | Description |
---|---|---|---|
Numerical Analysis | EEEN30101 | Pre-Requisite | Recommended |
Aims
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
On successful completion of the course, a student will be able to:
ILO 1: Understand the motivation and benefit of using convex optimization and machine learning
ILO 2: Establish a good understanding about convex sets and convex functions
ILO 3: Recognise typical forms of convex optimizations and their associated optimal solutions
ILO 4: Understand fundamental machine learning approaches in problem solving
ILO 5: Able to apply machine learning methods in practical data-oriented problems
ILO 6: 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% |
Examination
Duration: 3 hours. (70%)
Coursework
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 |