Learning Outcomes:
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
- Analyse the effectiveness of an ML model using classification tables, accuracy, recall and precision.
Part 3 – Neural Networks and Generative AI
After completing this part of the module, a student should be able to:
- Describe a neural network and its components.
- Develop a basic neural network for a business problem
- 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