Module Catalogue 2024/25

Pre Requisite Comment

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Co Requisite Comment

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Aims

Data science is an interdisciplinary field that uses Machine Learning methods, processes, algorithms, and systems to extract knowledge and insights from many structured and unstructured data. In the past decade, data analytics using machine learning has given us numerous ideas for optimisation in areas like manufacturing process, performance, quality assurance, and defect tracking, predictive and conditional maintenance, demand and throughput forecasting, automation control and energy sustainability.

Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. It involves the process of developing a model to find and learn the patterns embedded within a given big dataset and distinguish relevance from the irrelevant events. Such a process will require both statistical modelling and probabilistic reasoning approaches to be successful.

This module aims to provide a broad introduction to machine learning, data mining, and statistical pattern recognition technique to analyse information obtained from sensors such as images, sequential and categorical data

Outline Of Syllabus

1. Introduction to Machine Learning (ML): Concepts of Data Representation, Supervised
and Unsupervised Learning.
2. Basics: Naïve Bayesian Theorem, Linear prediction: Regression, Maximum Likelihood,
Regularisation.
3. Supervised Learning: Linear Classification, Logistic Regression (LR), Support Vector
Machines (SVM), Kernels, K-nearest Neighbours Classifier (KNN).
4. Neural Networks Representation and Backpropagation.
5. Optimisation Methods for ML System Design.
6. Unsupervised learning: Clustering.
7. Dimensionality Reduction (Feature Extraction): Principle Component Analysis (PCA),
Linear Discriminant Analysis (LDA).
8. Recommendation systems.
9. Data Science Ethics

Intended Knowledge Outcomes

At the end of the module, students should:
•       Have knowledge of the fundamental issues and challenges of machine learning (ML) such
as data and model selection, model complexity, etc.
•       Have knowledge of the pros and cons of many popular ML approaches.
•       Recognise the underlying mathematical relationships and learning paradigms across
various ML algorithms.
•       Apply various ML algorithms to a range of real-world data-driven applications.
•       Employ a good ethical framework during data acquisition and analysis.

Intended Skill Outcomes

At the end of the module, students should be able to:
•       Prepare, manipulate, and visualise data using appropriate methods for machine learning.
•       Explain the working principles of accessible machine learning (ML) algorithms.
•       Develop and deploy programs with appropriate ML algorithms on given datasets for
data-driven analysis.
•       Recognise the ethical considerations regarding the privacy and control of data.

Teaching Rationale And Relationship

The teaching is conducted via lectures and tutorial with small group discussions during class. This is complemented with self-study and preparation of tutorial solutions, coursework/project, and final examination to provide feedback on student learning. Teaching materials are made available to the students online for self-study and preparation in their own pace. Tutorial classes enable students to ask questions and clarify any doubts.

Due to the emerging Covid-19 situation, it is likely that some or all of the classes are conducted online. Attendance will be taken irrespective of whether the class is online or face-to-face, and students are expected to switch on their camera for online classes.

Assessment Rationale And Relationship

The written exam enables students to demonstrate their understanding and apply relevant knowledge and skills learnt to solve data analytical problems. The coursework assignment will provide students with hands-on opportunity to apply machine learning techniques on real-world data problem and generate meaningful analytical solutions to make sound data-driven decisions on practical issues

General Notes

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