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COMP60028 Demystifying Machine Learning: Theory and Applications



What is Machine Learning really? And how can it be applicable to you and others around you? This module aims to provide you with an intuitive understanding of the foundations of Machine Learning and how it can be applied to different disciplines. Besides learning the theory behind basic Machine Learning concepts, you will also apply what you have learnt to identify a user problem that can potentially be solved using Machine Learning and deliver a proof of concept of your proposed solution. By the end of the module, you will be able to cut through the Machine Learning hype in the mass media and explain to others what Machine Learning really is all about!

You will learn about the dynamics of machine learning and the different learning systems that this technology employs. You will look at this topic through two lenses; theory and application. You will work both independently and in a group setting and utilize your presentation and research skills.

Learning Outcomes

By the end of this module, you will better be able to:

Describe core machine learning principles, models and algorithms
Explain basic machine learning concepts to different audiences, such as laypeople, domain experts, and those with a more technical background
Identify problems and opportunities in multiple disciplines, including your own, that might benefit from machine learning
Apply machine learning principles and algorithms to solve a problem in an applied discipline, and critically evaluate other people’s solutions
Reflect on how machine learning is applicable to you at a personal level, within your surrounding context and the wider context


The module will cover theoretical foundations of machine learning concepts, models, and algorithms. You will aim to gain an intuitive understanding of topics such as:

What machine learning is all about
The importance of data and data pre-processing in machine learning
Evaluating machine learning systems
Models and algorithms for supervised learning, such as linear models (linear and logistic regression) and non-linear models (e.g. neural networks)
Models and algorithms for unsupervised learning problems like clustering and density estimation
You will also apply your knowledge to gain hands-on experience in solving problems in a specific discipline.
Exam Duration: N/A
Exam contribution: 100%
Coursework contribution: 0%

Term: Spring

Closed or Open Book (end of year exam): N/A

Coursework Requirement:

Oral Exam Required (as final assessment): N/A

Prerequisite module(s): None required

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Book List: