11318 modules
Page 241
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MATH6184 2026-27
Computational Machine Learning and Optimisation
This module will introduce you to some of the main approaches used for data analysis and machine learning. Students will gain knowledge and understanding of different computational machine learning methods, and gain skills in applying them to analyse data, make predictions, and evaluate performance.
The main tools to train and tune machine learning models stem from the area of nonlinear programming. Nonlinear programming is also used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques, solution algorithms, and their application in machine learning. -
MATH6184 2027-28
Computational Machine Learning and Optimisation
This module will introduce you to some of the main approaches used for data analysis and machine learning. Students will gain knowledge and understanding of different computational machine learning methods, and gain skills in applying them to analyse data, make predictions, and evaluate performance.
The main tools to train and tune machine learning models stem from the area of nonlinear programming. Nonlinear programming is also used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques, solution algorithms, and their application in machine learning. -
MATH6184 2030-31
Computational Machine Learning and Optimisation
This module will introduce you to some of the main approaches used for data analysis and machine learning. Students will gain knowledge and understanding of different computational machine learning methods, and gain skills in applying them to analyse data, make predictions, and evaluate performance.
The main tools to train and tune machine learning models stem from the area of nonlinear programming. Nonlinear programming is also used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques, solution algorithms, and their application in machine learning. -
MATH6184 2028-29
Computational Machine Learning and Optimisation
This module will introduce you to some of the main approaches used for data analysis and machine learning. Students will gain knowledge and understanding of different computational machine learning methods, and gain skills in applying them to analyse data, make predictions, and evaluate performance.
The main tools to train and tune machine learning models stem from the area of nonlinear programming. Nonlinear programming is also used in a variety of applications, ranging from machine learning and data science to finance and engineering. This course provides an introduction to nonlinear programming and covers modelling techniques, solution algorithms, and their application in machine learning. -
MATH1062 2027-28
Computational Mathematics
Computational techniques in mathematics, and mathematical approaches to computing, are central to advances across scientific fields. This module will introduce fundamental techniques in optimisation and numerical linear algebra, together with the computational essentials needed for mathematical study. -
MATH1062 2026-27
Computational Mathematics
Computational techniques in mathematics, and mathematical approaches to computing, are central to advances across scientific fields. This module will introduce fundamental techniques in optimisation and numerical linear algebra, together with the computational essentials needed for mathematical study. -
MATH1062 2025-26
Computational Mathematics
Computational techniques in mathematics, and mathematical approaches to computing, are central to advances across scientific fields. This module will introduce fundamental techniques in optimisation and numerical linear algebra, together with the computational essentials needed for mathematical study. -
MANG6542 2025-26
Computational Methods for Logistics
The overreaching goal of this module is to develop your quantitative problem solving skills by improving your algorithmic thinking and computer implementation skills. The module aims to provide you with in-depth knowledge about the contemporary optimisation methods, their strengths, efficient implementations and application areas, usability and shortcomings. The module emphasises the versatility of the methods, and encourages you to apply these techniques to diverse areas of business, in order to reorient your thinking processes towards a perspective of continuous improvement of every process. -
MANG6542 2027-28
Computational Methods for Logistics
The overreaching goal of this module is to develop your quantitative problem solving skills by improving your algorithmic thinking and computer implementation skills. The module aims to provide you with in-depth knowledge about the contemporary optimisation methods, their strengths, efficient implementations and application areas, usability and shortcomings. The module emphasises the versatility of the methods, and encourages you to apply these techniques to diverse areas of business, in order to reorient your thinking processes towards a perspective of continuous improvement of every process. -
MANG6542 2026-27
Computational Methods for Logistics
The overreaching goal of this module is to develop your quantitative problem solving skills by improving your algorithmic thinking and computer implementation skills. The module aims to provide you with in-depth knowledge about the contemporary optimisation methods, their strengths, efficient implementations and application areas, usability and shortcomings. The module emphasises the versatility of the methods, and encourages you to apply these techniques to diverse areas of business, in order to reorient your thinking processes towards a perspective of continuous improvement of every process.