Applied Machine Learning MSc

Program Overview

The Applied Machine Learning MSc program equips students with the skills to design, implement, and evaluate machine learning systems in electrical and electronic engineering domains. Through core modules, optional specializations, and an individual research project, students gain practical experience and deepen their understanding of modern machine learning methods. Graduates are highly sought after in industries requiring intelligent signal and data processing expertise, including robotics, computing, and telecommunications.

Program Outline

Applied Machine Learning MSc Program Details:

Degree Overview:

• Objective: To equip students with the knowledge and skills to design, implement, and evaluate machine learning systems.
• Description: This program delves into the theory and practice of modern machine learning methods, focusing on their application in electrical and electronic engineering domains.
Students will gain practical skills through lectures, tutorials, and labs, and apply their knowledge to real-world systems involving signals, sensors, and hardware.
• Specialization: The program offers specialized modules to enhance understanding of specific machine learning applications, including AI, computer vision, robotics, and signal processing.
• Individual Project: Students complete an individual research project, encouraging them to develop their own ideas and approaches to machine learning.
• Industry Relevance: The skills gained on this course are highly valued in various industries, including telecommunications, energy, healthcare, and logistics.
Graduates also have the opportunity to pursue further research.

Outline:

• Core Modules:
• Machine Learning:
Covers the theory and practice of modern machine learning methods, emphasizing how machine learning helps computers extract information automatically from data.
• Lab in AML: Develops essential skills for conducting group research projects in deep learning, applied to electrical and electronic engineering problems.
• AML Devices: Involves group research, design, and construction of a novel hardware device capable of making intelligent decisions.
• Optional Modules:
• Adaptive Signal Processing and Machine Intelligence:
Provides deeper insight into the applicability of modern methods for spectral estimation and machine intelligence techniques.
• Computer Vision and Pattern Recognition: Explores the concepts, formulations, and applications of pattern recognition.
• Digital Image Processing: Evaluates fundamental digital image processing methods from a signal processing perspective.
• Machine Reasoning: Offers insights into key artificial intelligence concepts and the analysis of algorithms.
• Optimisation: Covers finite-dimensional optimization theory, teaching students to formulate optimization problems, design algorithms for finding minima and maxima, and modify algorithms in standard computer packages.
• Probability and Stochastic Processes: Develops analytical skills for studying random phenomena in engineering systems and setting up probabilistic models for engineering problems.
• Speech Processing: Introduces signal processing and statistical techniques used in processing speech signals and their applications.
• Self-Organising Multi-Agent Systems: Addresses the challenges of engineering complex socio-technical and cyber-physical systems to support a "digital society."
• Topics in Control Systems: Expands knowledge of advanced modern control methodologies, including Kalman filtering and tracking, fault detection and isolation, and linear matrix inequalities.
• Topics in Large Dimensional Data Processing: Familiarizes students with the theory and design of algorithms for acquiring and processing large-dimensional data in areas like finance and the internet.
• Wavelets, Representation Learning and their Applications: Deepens understanding of wavelet theory and its effectiveness in extracting essential information.
• Individual Project: A significant research project where students develop a machine learning approach in the electrical and electronic engineering space.
Encourages original thinking and exploration of areas of interest.

Assessment:

• Methods:
• Coursework
• Examinations
• Practical assessments
• Individual research project
• Balance:
• 50% Coursework
• 40% Examinations
• 10% Practical

Teaching:

• Methods:
• Virtual learning environment
• Lectures
• Seminars
• Tutorials
• Laboratory work
• Independent study
• Individual and group projects
• Faculty:
• Course Director: Professor Krystian Mikolajczyk
• Academic Lead: Dr Ad Spiers
• Unique Approaches:
• The program emphasizes practical skills development through hands-on labs and projects.
• The individual research project allows students to explore their own interests and contribute to the field of machine learning.

Careers:

• Potential Paths:
• Robotics
• Computing
• Communications
• Telecommunications
• Energy
• Healthcare
• Logistics
• Opportunities:
• Graduates are highly sought after in a wide range of sectors.
• The program prepares students for careers requiring intelligent signal and data processing design, analysis, and control.
• Students can also pursue further research in the field.

Other:

• Life as an Applied Machine Learning MSc Student: The program website features a video showcasing the student experience.

• Home fee: £22,250
• Overseas fee: £41,750
Your fee is based on the year you enter the university, not your year of study. Whether you pay the Home or Overseas fee depends on your fee status. This is assessed based on UK Government legislation and includes things like where you live and your nationality or residency status. For courses starting on or after 1 August 2024, the maximum amount is £12,471. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs. The loan is not means-tested and you can choose whether to put it towards your tuition fees or living costs.