Course Details in 2024/25 Session

Module Title	LM Advanced Machine Learning
School	Mathematics
Department	Mathematics
Module Code	06 40200
Module Lead	TBC
Level	Masters Level
Credits	10
Semester	Semester 2
Pre-requisites
Co-requisites
Restrictions	None
Contact Hours	Lecture-24 hours Guided independent study-76 hours Total: 100 hours
Exclusions
Description	This module is designed to introduce students the cutting-edge realms of machine learning, offering an in-depth exploration of key topics that are at the forefront of this dynamic field. The curriculum will delve into advanced concepts, including generative models, reinforcement learning, and unsupervised learning, providing students with a profound understanding of these transformative technologies. Generative models will empower students to create data and explore the fascinating world of artificial creativity, enabling the generation of realistic content such as images and text. Reinforcement learning will equip them with the skills to design intelligent agents capable of learning and making decisions through interaction with their environment, with applications ranging from robotics to game playing. Unsupervised learning will unlock the secrets of discovering hidden patterns and structures in data, a critical skill in data analysis and pattern recognition. This module will not only impart theoretical knowledge but also offer practical hands-on experience, ensuring that students are well-prepared to harness the full potential of these advanced machine learning techniques in real-world applications. It's a pathway to becoming proficient in the forefront of AI and machine learning technologies, making graduates highly sought-after in the ever-evolving landscape of technology and data science.
Learning Outcomes	By the end of the module students should be able to: Understand theoretical and computational considerations in the advanced machine learning methods. Develop the skills of designing efficient computer algorithms for implementing these advanced machine learning methods. Understand the mathematics underlying these advanced machine learning methods. Develop the skill of applying these methods to complex real-word applications.
Assessment
Assessment Methods & Exceptions	Assessment: 80% examination, 20% coursework. Reassessment: Resit exam
Other
Reading List