COMP47590 Advanced Machine Learning

Academic Year 2022/2023

COMP47590 is an advanced module on Machine Learning that builds on the core concepts covered in COMP47490 or COMP47460. Either COMP47490 or COMP47460 is a prerequisite for this module. This module covers advanced, state of the art topics in machine learning in areas such as deep learning, ensemble methods, semi-supervised learning, human-in-the-loop machine learning, unsupervised machine learning, and reinforcement learning. Significant prior programming experience is essential (ideally in Python (with associated machine learning libraries), but experience in Java, C/C++, etc and a willingness to learn Python independently would suffice).

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Curricular information is subject to change

Learning Outcomes:

On completion of this module, students will be able to: 1) Distinguish between the different categories of machine learning algorithms; 2) Identify suitable machine learning approaches for different tasks, including state-of-the-art methods; 3) Run and evaluate a range of algorithms for different tasks using a standard machine learning toolkit; 4) Implement and evaluate machine learning algorithms in a high-level language.

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning








Approaches to Teaching and Learning:
The main teaching and learning approaches used in this module are lectures, lab work, task-based learning, group work and student-led readings. 
Requirements, Exclusions and Recommendations
Learning Requirements:

Having previously completed an introductory machine learning module (such as COMP47490, COMP47460, or COMP47750) is a prerequisite for this module.
To complete the continuous assessment, this module requires significant prior programming experience, ideally in Python.

Learning Recommendations:

Students should have strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.

Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Continuous Assessment: Machine learning assignments. Varies over the Trimester n/a Alternative linear conversion grade scale 40% No


Examination: End of Term Exam 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

feedback will be provided following assignment submission and correction.

Name Role
Mr Misgina Hagos Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 15:00 - 15:50
Laboratory Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Thurs 16:00 - 16:50
Laboratory Offering 1 Week(s) - 20, 25, 26, 29, 31, 32, 33 Thurs 17:00 - 17:50
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 13:00 - 13:50