Learning Outcomes:
Overall
After completing this module, a student should be able to:
- Describe Machine Learning and Artificial Intelligence tools and technologies.
- Identify business uses for AI and ML technology.
- Evaluate the outputs and effectiveness of ML and AI models in terms of their usefulness in business decision making and processes.
- Identify bias, legal requirements, ethical problems and business issues with using ML and AI technologies in business contexts.
Part 1 – Introduction to Machine Learning in Accountancy
After completing this part of the module, a student should be able to:
- Define Artificial Intelligence (AI), Machine Learning (ML), Deep Learning and Data Science.
- Enumerate the advantages of using ML technologies.
- Identify the major uses of ML technologies in business.
Part 2 – Machine Learning Concepts and Techniques
After completing this part of the module, a student should be able to:
- Describe the concepts that are used in AI/ML/DS.
- Identify the steps in a data science project.
- Evaluate the output of a DS project.
- Identify ethical concerns that arise from data problems and bias.
- Develop basic python programming skills to collect, manage and analyse data.
- Develop basic python programs to analyse accounting data.
- Develop basic python programs to create visualizations suitable for Exploratory Data Analysis (EDA.)
- Appreciate how python libraries can be used to build ML models.
- Recognise appropriate applications for linear regression and analyse how it produces solutions.
- Recognise appropriate applications for logistic regression and analyse how it produces solutions.
- Analyse the effectiveness of an ML model using R2, classification tables, accuracy, recall and precision.
Part 3 – Neural Networks
After completing this part of the module, a student should be able to:
- Describe a neural network and its components.
- Describe a perceptron, an activation function and a loss function.
- Describe how neural networks are structured. Describe the training loop and distinguish between training and inference.
- Develop a basic neural network for a business problem.
Part 4 – Generative AI
After completing this part of the module, a student should be able to:
- Explain, in general terms, how LLMs are developed and trained. Define tokenization, embeddings, transformer layers, context window, retrieval augmented generation and fine-tuning.
- Identify where Gen. AI services can be sourced and developed by businesses.
- Describe the techniques used to solve business problems using generative AI.
- Discuss the potential and problems of using generative AI in business.
- Identify ethical concerns that arise from using ML and AI in business.
Part 5 – AI Ethics and Legal Framework
After completing this part of the module, a student should be able to:
- Describe the requirements of the EU AI Act and identify its impact on business operations using AI.