Course Code: AIB309
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Synopsis

This course provides a rigorous introduction to deep learning with an emphasis on both foundational concepts and practical problem-solving in business contexts. It equips students with the knowledge and skills to understand, design, and implement key neural network architectures, including Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM) Networks, and Transformers. Students will learn representation learning, model training and optimisation, and performance evaluation, and will develop intuition for choosing and adapting architectures to different types of business-relevant tasks and data. The course balances theory and practice, combining conceptual discussions with hands-on exercises and case-based examples. By the end of the course, students will be able to reason about when and how deep learning methods can be appropriately applied in business scenarios, implement deep learning models using Python, interpret results, and effectively communicate the implications of model behaviours.
Level: 3
Presentation Pattern: EVERY REGULAR SEMESTER

Topics

  • Introduction to Deep Learning and Neural Networks
  • Deep Learning Applications in Business Contexts
  • Deep Learning Workflows Using Off-the-Shelf Frameworks
  • Introduction to Multilayer Perceptron (MLP)
  • MLP Modelling in Python for Business Applications
  • Introduction to Convolutional Neural Networks (CNN)
  • CNN Modelling in Python for Business Applications
  • Introduction to Recurrent Neural Networks (RNN)
  • RNN Modelling in Python for Business Applications
  • Long Short-Term Memory (LSTM) Networks as an Extension of RNN
  • Introduction to Transformer Models
  • Deep Learning Misuse and Ethical Issues

Learning Outcome

  • Discuss various aspects of deep learning in business applications
  • Recommend appropriate deep learning methods to solve business problems
  • Differentiate various neural network architectures and their impacts on business applications
  • Develop an end-to-end process of deep learning workflows in Python
  • Construct deep learning models using Python to solve business problems
  • Analyse deep learning outputs to generate business insights