Module overview
This course focuses on neural networks, machine learning fundamentals, and deep learning techniques, covering supervised and unsupervised learning, reinforcement learning, and architectures like CNNs and RNNs. Practical applications in business, such as predictive analytics, automation, customer insights, fraud detection, and financial modelling, are emphasized. Ethical considerations, bias mitigation, and model interpretability are explored. Case studies from finance, marketing, operations, and supply chain optimization illustrate AI-driven strategies, ensuring students gain hands-on experience with relevant tools and frameworks.
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Demonstrate how new technologies, including AI can shape businesses and societies. Classify AI technologies, evaluate solutions and redesign processes and business models to utilize them.
- Demonstrate proficiency in the use of AI tools and technologies for data analysis, aiding in the strategic decision-making process across various business contexts.
- Demonstrate practices that are ethical, responsible and sustainable.
- Reflect on how AI can support people and processes in an organization.
- Effectively communicate complex AI and management concepts, research findings, and strategic solutions to a variety of stakeholders.
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- How AI technologies can drive innovation and efficiency across various sectors, enhancing business strategies and competitive advantage.
- The ethical, social, and economic implications of AI deployment in business, emphasizing responsible management and decision-making.
- Demonstrate proficiency in the use of AI tools and technologies for data analysis, aiding in the strategic decision-making process across various business contexts.
- The role and impact of AI in modern business practices, including its integration into business models and operations.
- The core principles and practices of business and management, providing a solid foundation for leadership in business.
- The analytical techniques necessary for AI-driven decision making, enabling effective strategy and problem-solving in complex business environments.
- How business analytics can inform decisions and drive a business by utilizing both AI and statistical methods.
Transferable and Generic Skills
Having successfully completed this module you will be able to:
- Use computing and IT resources effectively.
- Use computing and IT resources effectively.
- Utilize critical thinking and AI-enhanced problem-solving skills across diverse business scenarios, preparing you to address and resolve challenges swiftly and efficiently.
- Use library and other resources, including the application of bibliographical skills.
- Demonstrate effective project management skills, including the ability to plan, execute, and manage both time and resources in AI-driven projects, ensuring successful outcomes.
- Use library and other resources, including the application of bibliographical skills.
- Employ digital literacy skills, particularly in utilizing AI technologies and data analysis tools, to drive decision-making and innovation in business practices.
- Adapt to and manage change at an increasingly faster rate, a vital skill in the rapidly evolving field of AI and management.
- Evaluate and select AI solutions for complex problems by distinguishing the real value from the hype.
Syllabus
The exact topics covered in this module will depend on the configuration of the team of tutors and their respective research areas within strategy and innovation management. The module may include, but is not limited to, the following topics:
· Neural networks, machine learning, and deep learning techniques in business contexts.
· Data pre-processing, model selection, training, and evaluation,
· Leverage predictive analytics, automation, and customer behaviour modelling
· Practical proficiency in tools like TensorFlow or PyTorch
Learning and Teaching
Teaching and learning methods
Teaching methods include: Lectures, interactive case studies, simulation game, directed reading, and private/guided study. Learning activities include:
• Introductory lectures
• A groupwork: presentation
• Case study/problem solving activities
• Private study: argumentative essay
• Use of video and online materials
Class activities, such as problem solving activities, discussions and use of case studies will provide opportunities for you to gain feedback from you tutor and/or peers about their level of understanding and knowledge prior to any formal summative assessment.
| Type | Hours |
|---|---|
| Lecture | 24 |
| Seminar | 10 |
| Independent Study | 116 |
| Total study time | 150 |
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
| Method | Percentage contribution |
|---|---|
| Assignment | 70% |
| Group Case Study | 30% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
| Method | Percentage contribution |
|---|---|
| Individual report | 100% |
Repeat
An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.
| Method | Percentage contribution |
|---|---|
| Individual report | 100% |