• Introduction to the module and faculty, programme flow and pre-assignments.
• Project instructions, project briefing and assignment of groups
• Assignment of prework
• Learning of key concepts from pre-reading material set by professors
• Preparation of cases set by professors
• Identification of personal learning goals of the current module set by oneself

• Learn the Three Components of Machine Learning: Data, Model and Loss
• Understand AI through groupwork by modelling real-life situation
• Deep dive into Machine Learning: Deep Learning, Model Validation and Selection, unsupervised machine learning
• Learn to use clustering methods for grouping large collections of data points into few coherent clusters
• Learn to use dimensionality reduction methods to learn relevant features of a data point
• Acquire hands-on experience in applying basic methods in AI through groupwork
• Explore opportunities and challenges of applying AI in business

• Overview and history of AI
• Understanding the relationship between data and AI
• Discussion of case studies on the use of AI in business
• Algorithms for recommending items for consumers
• Approaches for detecting anomalies in data
• Hands-on exercise

• Understanding and processing text in business
• Tools for reinforcement learning
• Hands-on exercise
• Methods for explaining outputs from AI systems
• Understanding the limitations of AI
• AI Ethical and Societal Issues
• Wrap up and next step

• Project presentation

Multiple methods of assessment are used to provide an opportunity for the participant to demonstrate their learning results with a variety of learning styles.

These include:

• Pre-assignment due before in-class session
• Class contribution
• In-class assignments (group work and presentations during the module)
• Take-home assignment due after in-class session

Upon completion and satisfying the requirements of passing this course, learners will be awarded 12 subject credits.