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The module is designed for graduates in any science discipline embarking on postgraduate studies in Ocean and Earth Science.

This module is the introductory module in the International Transfer BM programme, specifically designed to introduce international transfer students to medicine in Southampton.

The BM programmes are however highly contextualised and integrated programmes in which the application of knowledge and understanding, clinical skills and professional practice applicable to medicine are learned through a range of modules none of which are stand- alone. Therefore this module should be recognised by students and teachers alike as part of the whole year and programme. As is the nature of clinical placements, the exact learning experiences of each student will be variable. However, all students will receive the same broad opportunities sufficient to achieve the learning outcomes of the module, and it is expected that students will take responsibility for making the most of the opportunities provided and being pro-active in securing experiences in areas in which they feel they are weak and/or they have had least learning experiences.

This module is the introductory module in the International Transfer BM programme, specifically designed to introduce international transfer students to medicine in Southampton.

The BM programmes are however highly contextualised and integrated programmes in which the application of knowledge and understanding, clinical skills and professional practice applicable to medicine are learned through a range of modules none of which are stand- alone. Therefore this module should be recognised by students and teachers alike as part of the whole year and programme. As is the nature of clinical placements, the exact learning experiences of each student will be variable. However, all students will receive the same broad opportunities sufficient to achieve the learning outcomes of the module, and it is expected that students will take responsibility for making the most of the opportunities provided and being pro-active in securing experiences in areas in which they feel they are weak and/or they have had least learning experiences.

This course lays the foundation for further studies in commercial and maritime law by introducing you to advanced rules and applications of contract law. We will be exploring some key areas of commercial law, such as the law of agency and the sale of goods. The course trains your commercial analytical skills by exposing you to a range of complex relationships which often involve numerous parties and transactions, and by understanding how the law settles these in light of the reasonable expectation of the parties, commercial expedience, the integrity of legal concepts as well as wider policy factors.

This course lays the foundation for further studies in commercial and maritime law by introducing you to advanced rules and applications of contract law. We will be exploring some key areas of commercial law, such as the law of agency and the sale of goods. The course trains your commercial analytical skills by exposing you to a range of complex relationships which often involve numerous parties and transactions, and by understanding how the law settles these in light of the reasonable expectation of the parties, commercial expedience, the integrity of legal concepts as well as wider policy factors.

Differentiable Programming and Deep learning has revolutionised numerous fields in recent years. We’ve witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of cheap GPGPU compute coupled with large datasets and some neat algorithms. More broadly, the idea of ‘Differentiable Programming’, in which we define entire programs as compositions of differentiable operations which can then be optimised to fit data, looks to become a new norm in how we utilise computers.

This module will look at how deep learning and differentiable programming works, from theoretical foundations right through to practical implementation. We’ll study key aspects such as automatic differentiation, look at models for deep learning such as convolutional and recurrent neural networks and `transformer’ architectures, as well as considering current research in depth. Along the way we’ll also look at aspects of biology and neuroscience, and see how ideas from these fields feed-in to current research.

The overall aim of this module is not to teach you to be able to train pre-existing models (although you will learn to do that!), but rather to equip you with the fundamental skills to be able to understand and implement models and ideas that are currently being developed by researchers. We intend to equip you with the knowledge needed to understand new ideas as they are published, and give you the ability to constructively criticise, and identify limitations, of different approaches.

As a word of warning, this is a mathematical module: the predominant focus is on looking at models that can be optimised via gradient methods. You need to have a good grasp of linear (matrix) algebra and matrix calculus, as well as the fundamentals of machine learning, probability and statistics. You will also necessarily be comfortable with Python programming and the use of numeric/matrix libraries such as numpy or pytorch. As such, the Foundations of Machine Learning module is a prerequisite.

Differentiable Programming and Deep learning has revolutionised numerous fields in recent years. We’ve witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of cheap GPGPU compute coupled with large datasets and some neat algorithms. More broadly, the idea of ‘Differentiable Programming’, in which we define entire programs as compositions of differentiable operations which can then be optimised to fit data, looks to become a new norm in how we utilise computers.

This module will look at how deep learning and differentiable programming works, from theoretical foundations right through to practical implementation. We’ll study key aspects such as automatic differentiation, look at models for deep learning such as convolutional and recurrent neural networks and `transformer’ architectures, as well as considering current research in depth. Along the way we’ll also look at aspects of biology and neuroscience, and see how ideas from these fields feed-in to current research.

The overall aim of this module is not to teach you to be able to train pre-existing models (although you will learn to do that!), but rather to equip you with the fundamental skills to be able to understand and implement models and ideas that are currently being developed by researchers. We intend to equip you with the knowledge needed to understand new ideas as they are published, and give you the ability to constructively criticise, and identify limitations, of different approaches.

As a word of warning, this is a mathematical module: the predominant focus is on looking at models that can be optimised via gradient methods. You need to have a good grasp of linear (matrix) algebra and matrix calculus, as well as the fundamentals of machine learning, probability and statistics. You will also necessarily be comfortable with Python programming and the use of numeric/matrix libraries such as numpy or pytorch. As such, the Foundations of Machine Learning module is a prerequisite. You’ll also be expected to read and try to understand scientific papers along the way.

The module will equip you with the skills needed to start to understand the motivation of the latest deep learning research, and to start to critically analyse this.

Differentiable Programming and Deep learning has revolutionised numerous fields in recent years. We’ve witnessed improvements in everything from computer vision through speech analysis to natural language processing as a result of the advent of cheap GPGPU compute coupled with large datasets and some neat algorithms. More broadly, the idea of ‘Differentiable Programming’, in which we define entire programs as compositions of differentiable operations which can then be optimised to fit data, looks to become a new norm in how we utilise computers.

This module will look at how deep learning and differentiable programming works, from theoretical foundations right through to practical implementation. We’ll study key aspects such as automatic differentiation, look at models for deep learning such as convolutional and recurrent neural networks and `transformer’ architectures, as well as considering current research in depth. Along the way we’ll also look at aspects of biology and neuroscience, and see how ideas from these fields feed-in to current research.

The overall aim of this module is not to teach you to be able to train pre-existing models (although you will learn to do that!), but rather to equip you with the fundamental skills to be able to understand and implement models and ideas that are currently being developed by researchers. We intend to equip you with the knowledge needed to understand new ideas as they are published, and give you the ability to constructively criticise, and identify limitations, of different approaches.

As a word of warning, this is a mathematical module: the predominant focus is on looking at models that can be optimised via gradient methods. You need to have a good grasp of linear (matrix) algebra and matrix calculus, as well as the fundamentals of machine learning, probability and statistics. You will also necessarily be comfortable with Python programming and the use of numeric/matrix libraries such as numpy or pytorch. As such, the Foundations of Machine Learning module is a prerequisite. You’ll also be expected to read and try to understand scientific papers along the way.

The module will equip you with the skills needed to start to understand the motivation of the latest deep learning research, and to start to critically analyse this.

This compulsory module introduces you to enhanced fashion design principles and encourages you to develop an innovative and exploratory perspective on your studio practice, through a combination of projects and technical workshops. This considered approach is intended to further develop your interests and understanding of the creative and conceptual practical requirements associated with fashion design at an advanced level.

You will develop a sound awareness of your unique identity as a fashion design practitioner, through individual tutorials and group critiques designed to challenge and stimulate your knowledge and thinking of contemporary and emerging fashion ideas and debates.

This compulsory module introduces you to enhanced fashion design principles and encourages you to develop an innovative and exploratory perspective on your studio practice, through a combination of projects and technical workshops. This considered approach is intended to further develop your interests and understanding of the creative and conceptual practical requirements associated with fashion design at an advanced level.

You will develop a sound awareness of your unique identity as a fashion design practitioner, through individual tutorials and group critiques designed to challenge and stimulate your knowledge and thinking of contemporary and emerging fashion ideas and debates.