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COMP70014 Machine Learning for Imaging

Lecturer(s): Dr Iain Phillips


This module covers the fundamental concepts and advanced methodologies of machine learning for imaging and relates those to real-world problems in computer vision and medical image analysis. You will experience different approaches to machine learning including supervised and unsupervised techniques with an emphasis on deep learning methods. Applications include image classification, semantic segmentation, object detection and localisation, and registration. A key objective is to equip you with the skills needed to work in, and conduct research into, image computing and applied machine learning.

Learning Outcomes

Upon successful completion of this module you will be able to: - select and apply appropriate machine learning methods for solving practical problems in image computing - implement and assess techniques for image classification, regression, semantic segmentation, object detection and localisation in imaging data - compare, characterise and quantitatively assess competing approaches to computer vision and image computing - evaluate the performance of computer vision and image computing algorithms - analyse critically the limitations of machine learning techniques in the domain of image computing


Introduction to machine learning for imaging Image classification Image segmentation Object detection & localisation Image registration Generative models and representation learning Application to real-world problems
Exam Duration: N/A
Coursework contribution: 20%

Term: Spring

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

Coursework Requirement:
         To be announced

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

Prerequisite module(s): None required

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