AI and Machine Learning

This subject is available under ICMS undergraduate degrees, please click the button below to find an undergraduate course for you.

Subject Code:

DAT303A

Subject Type:

Specialisation 

Credit Points:

3 credit points

Pre-requisite/Co-requisite: 

ICT103A Programming Principles 

DAT203A Big Data Systems 

 Course level of study pre-requisite:  

a total of 24 credit points (15 credit points, including ICT101A, ICT102A, ICT103A, DAT101A from level 100 and 9 credit points from level 200 core subjects) prior enrolling into level 300 core and specialisation subjects. 

Subject Level:

300 

Subject Rationale:

The twenty-first century is perhaps best described as the dawn of evolutionary digital technologies such as artificial intelligence (AI). AI is a concept to create intelligent machines that can simulate the human thinking capability. Based on that, machine learning (ML) is a subset of AI aiming towards software that can perform a specific task without being explicitly programmed to do so. Applications of AI are widespread from automated and customised advertising to facial and voice recognition onto robotic manufacturing, and even self-driving cars. In fact, AI is fast becoming ubiquitous and indisputably indispensable to our everyday work and life. As a result, students of computer sciences and data analytics should have a broad understanding of how AI works and how it can be used to add value to business. This subject is designed to equip students with these competencies.  

 This subject offers a comprehensive introduction into the history and evolution of AI and ML. It then provides students with a detailed explanation of ML development, algorithms, models, data base requirements, and computing technologies as well as analytical techniques for supervised learning, unsupervised learning, and reinforcement learning each with real-world examples and applications. It will explain the applications of AI in areas such as fraud detection, image processing, facial recognition and NLP. Students will then be able to evaluate and elaborate different AI and ML techniques applicable in business in order to make an informed decisions on how they can be used to solve various business problems. Ethical and social considerations in the development, deployment and use of AI systems will be discussed as well. 

Learning Outcomes:

a) Explain the history and evolution of AI and how it relates to the domain of ML.

b) Critically evaluate supervised, unsupervised, and reinforcement ML algorithms and their characteristics.

c) Analyse appropriate business and technical requirements for the use of ML technologies to solve contemporary business problems taking into account ethical and social considerations

d) Assess and contextualise key concepts of ML and design and implement ML-based solutions for business contexts.

e) Appraise emerging trends and platforms that impact the adoption and successful development of new AI and ML tools across sectors.

Student Assessment:

Broad Topics to be Covered:

Please note that these topics are often refined and subject to change so for up to date weekly topics and suggested reading resources, please refer to the Moodle subject page.