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Artificial intelligence (AI) is transforming how financial institutions analyse data, manage risk, and make investment decisions. This module introduces students to the practical applications of AI and machine learning (ML) in modern banking and finance. It focuses on developing a working understanding of key methods, such as predictive modelling, natural language processing, portfolio management, and risk modelling, while emphasising interpretation, ethical use, operational considerations, and model governance. You will learn how to apply AI tools to real financial datasets to solve a range of financial decision problems, gaining experience in both the analytical design and evaluation of AI-based models. This includes an understanding of model risk, robustness, and explainability in real-world financial settings. The module aims to strike a balance between conceptual understanding and hands-on experience. To this end, we plan to employ accessible programming exercises using appropriate statistical and computational software tools commonly applied in financial analysis to illustrate how AI can extract value from complex financial data. By the end of the module, you will be able to design, evaluate, and communicate AI-based financial models with an appreciation of both your analytical power, practical limitations, and operational implications. The emphasis throughout is on practical relevance and employability – equipping you with the analytical, technical and governance-aware skills increasingly sought by asset managers, banks, investors, fintech firms, and regulators.

This module examines artificial intelligence through contemporary approaches to the management of projects and project-based organisations, drawing on multidisciplinary and multi-perspective viewpoints to understand how AI influences governance, decision-making, and performance across varied organisational and delivery contexts. It introduces a wider range of concepts that support critical engagement with AI-enabled change, enabling students to compare alternative approaches, recognise their benefits and limitations, and evaluate implications for organisations and stakeholders. The module develops students’ ability to critically evaluate approaches and formulate recommendations for professional practice. It also considers the wider roles that managers/leaders, and executives play in shaping responsible adoption, value realisation, and supports students in communicating conclusions to diverse stakeholders.

Why and how we should we regulate Artificial Intelligence [AI]? This module will systematically analyse this question with reference to existing AI laws, drawing on contemporary theoretical discourse, analytical frameworks, and a selection of case study investigations. AI is disrupting core industries and public policies with driverless cars, AI-enabled medical devices, autonomous weapons systems, personalised entertainment, digital artists, ‘intelligent’ virtual assistants, and artificial recruiters among its many applications. AI promises unprecedented potential to advance human interests. However, it also poses many risks. Current evidence suggests that some AI can misinform and manipulate human behaviour, violate individual privacy, increase socio-economic inequalities, and enhance bias in decision-making, even when used in good faith. Some even believe AI could pose an existential threat to a sustainable future. For example, acting as a double-edge sword, new AI-based environmental applications pledge to contribute to global sustainability objectives, but AI’s energy footprint raises concerns that it could impede progress on climate change.

In response to the rapid deployment of AI technology across numerous sectors, legislators and regulators enact diverse governance models to control the development, circulation and use of AI applications. Taking a pro-active approach, the European Union’s AI Act aims to introduce the world’s first comprehensive framework for regulating AI technology. In contrast, other jurisdictions depart from holistic approaches and favour sector-based regulatory interventions. These efforts seek to strike a balance between enabling progressive innovation and preventing AI causing harm to human sustainability.

This module will examine the design and implementation of current national and supranational efforts to regulate AI applications in specific areas, comparing their normative standards, institutional arrangements, and enforcement mechanisms. It will offer concepts, analytical frameworks, and methods for evaluating regulatory objectives, policy priorities, and outcomes. Moreover, it will investigate key ethical and socio-economic risks associated with the deployment of AI applications. The module adopts an approach that bridges theoretical inquiry and an examination of contemporary problematics arising with the use of AI in practice. In the first part, the module will place AI regulation within the theoretical discourse on regulating technology and examine current regulatory paradigms. In the second part, it will analyse a selection of specific case studies of AI uses and laws from diverse sectors.

Why and how we should regulate Artificial Intelligence (AI)? This module will systematically analyse these questions with reference to existing AI laws, drawing on contemporary theoretical discourse, analytical frameworks, and a selection of case-study investigations. AI is disrupting core industries and public policies with driverless cars, AI-enabled medical devices, autonomous weapons systems, personalised entertainment, digital artists, ‘intelligent’ virtual assistants, and artificial recruiters amongst its many applications. AI promises unprecedented potential to advance human interests. However, it also poses many risks. Current evidence suggests that some AI can misinform and manipulate human behaviour, violate individual privacy, increase socio-economic inequalities, and enhance bias in decision-making, even when used in good faith. Some even believe it could pose an existential threat to a sustainable future. For example, acting as a double-edge sword, new AI-based environmental applications pledge to contribute to global sustainability objectives, whilst, simultaneously, AI’s energy footprint raises concerns that it could impede progress on climate change.



In response to the rapid deployment of AI technology across numerous sectors, legislators and regulators enact diverse governance models to control the development, circulation and use of AI applications. Taking a pro-active approach, the European Union’s AI Act aims to introduce the world’s first comprehensive framework for regulating AI technology. In contrast, other jurisdictions depart from holistic approaches and favour sector-based regulatory interventions. These efforts seek to strike a balance between enabling progressive innovation and preventing AI causing harm to human sustainability.



This module will examine the design and implementation of current national and supranational efforts to regulate AI applications in specific areas, comparing their normative standards, institutional arrangements and enforcement mechanisms. It will offer concepts, analytical frameworks, and methods for evaluating regulatory objectives, policy priorities, and outcomes. Moreover, it will investigate key ethical and socio-economic risks associated with the deployment of AI applications. The module adopts an approach that bridges theoretical inquiry and an examination of contemporary problematics arising in with the use of AI in practice. In the first part, the module will place AI regulation within the theoretical discourse on regulating technology and examine current regulatory paradigms. In the second part, it will study a selection of specific case-studies of AI laws from diverse sectors.

Why and how we should we regulate Artificial Intelligence [AI]? This module will systematically analyse this question with reference to existing AI laws, drawing on contemporary theoretical discourse, analytical frameworks, and a selection of case study investigations. AI is disrupting core industries and public policies with driverless cars, AI-enabled medical devices, autonomous weapons systems, personalised entertainment, digital artists, ‘intelligent’ virtual assistants, and artificial recruiters among its many applications. AI promises unprecedented potential to advance human interests. However, it also poses many risks. Current evidence suggests that some AI can misinform and manipulate human behaviour, violate individual privacy, increase socio-economic inequalities, and enhance bias in decision-making, even when used in good faith. Some even believe AI could pose an existential threat to a sustainable future. For example, acting as a double-edge sword, new AI-based environmental applications pledge to contribute to global sustainability objectives, but AI’s energy footprint raises concerns that it could impede progress on climate change.

In response to the rapid deployment of AI technology across numerous sectors, legislators and regulators enact diverse governance models to control the development, circulation and use of AI applications. Taking a pro-active approach, the European Union’s AI Act aims to introduce the world’s first comprehensive framework for regulating AI technology. In contrast, other jurisdictions depart from holistic approaches and favour sector-based regulatory interventions. These efforts seek to strike a balance between enabling progressive innovation and preventing AI causing harm to human sustainability.

This module will examine the design and implementation of current national and supranational efforts to regulate AI applications in specific areas, comparing their normative standards, institutional arrangements, and enforcement mechanisms. It will offer concepts, analytical frameworks, and methods for evaluating regulatory objectives, policy priorities, and outcomes. Moreover, it will investigate key ethical and socio-economic risks associated with the deployment of AI applications. The module adopts an approach that bridges theoretical inquiry and an examination of contemporary problematics arising with the use of AI in practice. In the first part, the module will place AI regulation within the theoretical discourse on regulating technology and examine current regulatory paradigms. In the second part, it will analyse a selection of specific case studies of AI uses and laws from diverse sectors.

This module introduces you to some of the key areas of the international music industry that concern artists and their repertoires. It focuses on infrastructure (artists, repertoires, distribution channels etc.) to help you understand the consumption of music as a practice on a global scale, and prepares you to manage your own career and that of others.

This module introduces you to some of the key areas of the international music industry that concern artists and their repertoires. It focuses on infrastructure (artists, repertoires, distribution channels etc.) to help you understand the consumption of music as a practice on a global scale, and prepares you to manage your own career and that of others.

EMHPs will assess children, young people and families with a range of common mental health problems. This assessment must reflect the child and their family’s perspective and must be conducted with the child’s and family’s needs paramount. The assessment should reflect a shared understanding of the child or young person’s current difficulties and inform how decisions are made with the family about the best next steps for the child and the family. Possible next steps include giving advice and psycho-education, referral to another agency, care within the multidisciplinary CAMHS team (e.g. for medication or formal psychological therapy) or a low intensity intervention (e.g. guided self-help, brief behavioural activation) delivered by the EMHP themselves.

An EMHP must be able to undertake a child-centred interview which identifies the child’s/ young person’s current difficulties, their goals and those of their family/parents, their strengths and resources, and any risk to self or others. They need to understand the child in the context of their family, culture, wider social environment, developmental stage and temperament. They need to engage the child or young person and their carer(s) and other family members and to establish therapeutic alliances. They will need to gather appropriate information from different sources, be able to make sense of this and with the family develop a shared understanding. They also need to understand how the child’s difficulties fit within a diagnostic framework, identify other physical, developmental or psychological difficulties (e.g. epilepsy, autistic spectrum disorders, attachment history) and know what evidence-based interventions are likely to be appropriate.

The module will therefore equip the EMHP with a good understanding of the incidence, prevalence and presentation of common mental health problems experienced by children and young people and evidenced-based treatment choices. Skills teaching will develop core competences in active listening, engagement, alliance building, patient-centred information gathering, information giving and shared decision-making. The module will develop the EMHPs competency in assess and identify areas of difficulty (including risk) and establish main areas for change, establish and maintain a working therapeutic alliance and engaging the child/young person/family to support them in self-management of recovery, Identify and differentiate between common mental health problems in CYP, Navigate and signpost to appropriate interventions and use routine outcome measures and standardised assessment tools effectively as part of the assessment and engagement process.

EMHPs will assess children, young people and families with a range of common mental health problems. This assessment must reflect the child and their family’s perspective and must be conducted with the child’s and family’s needs paramount. The assessment should reflect a shared understanding of the child or young person’s current difficulties and inform how decisions are made with the family about the best next steps for the child and the family. Possible next steps include giving advice and psycho-education, referral to another agency, care within the multidisciplinary CAMHS team (e.g. for medication or formal psychological therapy) or a low intensity intervention (e.g. guided self-help, brief behavioural activation) delivered by the practitioner themselves.

An EMHP must be able to undertake a child-centred interview which identifies the child’s/ young person’s current difficulties, their goals and those of their family/parents, their strengths and resources and any risk to self or others. They need to understand the child in the context of their family, culture, wider social environment, developmental stage and temperament. They need to engage the child or young person and their carer(s) and other family members and to establish therapeutic alliances. They will need to gather appropriate information from different sources, be able to make sense of this and with the family develop a shared understanding. They also need to understand how the child’s difficulties fit within a diagnostic framework, identify other physical, developmental or psychological difficulties (e.g. epilepsy, autistic spectrum disorders, attachment history) and know what evidence-based interventions are likely to be appropriate.

The module will therefore equip the EMHP with a good understanding of the incidence, prevalence and presentation of common mental health problems experienced by children and young people and evidenced-based treatment choices. Skills teaching will develop core competences in active listening, engagement, alliance building, patient-centred information gathering, information giving and shared decision-making. The module will develop the EMHPs competency in assess and identify areas of difficulty (including risk) and establish main areas for change, establish and maintain a working therapeutic alliance and engaging the child/young person/family to support them in self-management of recovery. Identify and differentiate between common mental health problems in CYP, Navigate and signpost to appropriate interventions and use routine outcome measures and standardised assessment tools effectively as part of the assessment and engagement process.

EMHPs will assess children, young people and families with a range of common mental health problems. This assessment must reflect the child and their family’s perspective and must be conducted with the child’s and family’s needs paramount. The assessment should reflect a shared understanding of the child or young person’s current difficulties and inform how decisions are made with the family about the best next steps for the child and the family. Possible next steps include giving advice and psycho-education, referral to another agency, care within the multidisciplinary CAMHS team (e.g. for medication or formal psychological therapy) or a low intensity intervention (e.g. guided self-help, brief behavioural activation) delivered by the EMHP themselves.

An EMHP must be able to undertake a child-centred interview which identifies the child’s/ young person’s current difficulties, their goals and those of their family/parents, their strengths and resources, and any risk to self or others. They need to understand the child in the context of their family, culture, wider social environment, developmental stage and temperament. They need to engage the child or young person and their carer(s) and other family members and to establish therapeutic alliances. They will need to gather appropriate information from different sources, be able to make sense of this and with the family develop a shared understanding. They also need to understand how the child’s difficulties fit within a diagnostic framework, identify other physical, developmental or psychological difficulties (e.g. epilepsy, autistic spectrum disorders, attachment history) and know what evidence-based interventions are likely to be appropriate.

The module will therefore equip the EMHP with a good understanding of the incidence, prevalence and presentation of common mental health problems experienced by children and young people and evidenced-based treatment choices. Skills teaching will develop core competences in active listening, engagement, alliance building, patient-centred information gathering, information giving and shared decision-making. The module will develop the EMHPs competency in assess and identify areas of difficulty (including risk) and establish main areas for change, establish and maintain a working therapeutic alliance and engaging the child/young person/family to support them in self-management of recovery, Identify and differentiate between common mental health problems in CYP, Navigate and signpost to appropriate interventions and use routine outcome measures and standardised assessment tools effectively as part of the assessment and engagement process.