Machine Learning module (CS31006)

Machine learning (ML) is a rapidly growing field that has the potential to revolutionise industries such as healthcare, finance, and transportation.

With a solid foundation in machine learning, you can analyse vast amounts of data, make predictions, or create intelligent agents. This enables you to implement innovative solutions to solve real-world problems.

Whether your passion is data science or software development, the ML skillset is highly sought after in both industries. A strong understanding of machine learning will help you future-proof your career and approach complex problem-solving settings.

What you will learn

In this module, you will:

• study supervised machine learning and its applications
• learn about classification and regression problems
• learn about important principles such as generalisation, data splits, and cross-validation
• explore common machine learning algorithms, such as K-nearest neighbour, and linear regression
• discuss parameter optimisation and commonly used optimisation algorithms
• study how neural networks work and the algorithms used to train them
• discuss deep learning and regularisation

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

• explain key theoretical concepts from machine learning such as generalisation and regularisation
• discuss well-known supervised learning algorithms
• train and evaluate machine learning methods using an industry standard software framework
• describe how to estimate predictive performance of supervised learning methods on a given dataset
• design and conduct machine learning experiments

Assignments / assessment

• supervised machine learning evaluation (50%)
• written exam (50%)

Teaching methods / timetable

You will learn by taking a hands-on approach. This will involve taking part in tutorials and practical sessions.

Learning material is provided through videos, review notes, examples, and tutorial questions.

This is a half-semester module. You will study another 10 credit module during the other half of this semester.