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Advanced Methods in Machine Learning (982G5)

15 credits, Level 7 (Masters)

Autumn teaching

On this module, you'll develop your knowledge and understanding of recent machine learning technologies: how they can be applied to different tasks, their benefits, limitations and open challenges.

Key topics include:

• deep generative models
• neural network architectures and inductive biases
• learning paradigms including supervised, self-supervised and semi-supervised
• challenges related to the dataset distribution.

These topics will be introduced in the context of common applications and tasks in areas such as computer vision and natural language processing. You will learn how to develop and critique solutions to real-world problems.

Contact hours and workload

This module is approximately 150 hours of work. This breaks down into about 44 hours of contact time and about 106 hours of independent study. The Â鶹´«Ã½ may make minor variations to the contact hours for operational reasons, including timetabling requirements.

We regularly review our modules to incorporate student feedback, staff expertise, as well as the latest research and teaching methodology. We’re planning to run these modules in the academic year 2024/25. However, there may be changes to these modules in response to feedback, staff availability, student demand or updates to our curriculum.

We’ll make sure to let you know of any material changes to modules at the earliest opportunity.