8443 modules
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STAT6143 2027-28
Machine Learning
The module aims to equip students with the necessary foundations to make practical and effective use of machine learning methods on complex datasets. This course uses R and is delivered as an intensive one-week module for the MSc in Data Analytics for Government. -
MATH6168 2027-28
Machine Learning
The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets. -
MATH6168 2028-29
Machine Learning
The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets. -
MATH3097 2027-28
Machine Learning
The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets. -
MATH6168 2025-26
Machine Learning
The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets. -
MATH6168 2029-30
Machine Learning
The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets. -
MATH3097 2028-29
Machine Learning
The purpose of the module will be to introduce students to the fundamentals of machine learning, i.e. computational methods for statistical learning, prediction and decision-making using data. The basic principles of predictive modelling will be outlined, and then demonstrated using various machine learning methods and appropriate data sets. -
AICE2006 2027-28
Machine Learning (I)
Machine Learning is about extracting useful information from large and complex datasets. The subject is a rich mixture of concepts from function analysis, statistical modelling and computational techniques. The module will introduce the fundamental principles of the subject, where you will learn the theoretical basis of how learning algorithms are derived and when they are optimally applied, and gain hands-on experience in laboratory-based sessions. -
AICE2006 2026-27
Machine Learning (I)
Machine Learning is about extracting useful information from large and complex datasets. The subject is a rich mixture of concepts from function analysis, statistical modelling and computational techniques. The module will introduce the fundamental principles of the subject, where you will learn the theoretical basis of how learning algorithms are derived and when they are optimally applied, and gain hands-on experience in laboratory-based sessions. -
SESA6093 2030-31
Machine Learning for Aerospace Engineering
This course is designed for students and researchers in academia and industry who are focused on advanced topics in aerospace engineering, particularly in aerodynamic loads and aeroelastic analysis predictions. It also caters to technical decision-makers who seek to understand emerging machine learning and projection-based techniques for future development strategies. The content is tailored to equip students with both foundational knowledge and practical skills, ensuring they can apply modern machine learning and reduced-order model techniques to real-world aerospace challenges and make informed decisions in research and industrial settings.