COMP47750 Machine Learning with Python

Academic Year 2021/2022

The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as to instruct students in the practical aspects of applying machine learning algorithms. Key techniques in supervised machine learning will be covered, such as classification using decision trees and nearest neighbour algorithms, and regression analysis. A particular emphasis will be placed on the evaluation of the performance of these algorithms. In unsupervised machine learning, a number of popular clustering algorithms will be presented in detail. Further topics covered include ensemble learning, dimension reduction, and recommender systems. This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts. Exercises and assignments will use the machine learning libraries in Python.

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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 a suitable machine learning algorithm for a given application or task;
3) Run and evaluate the performance of a range of algorithms on real datasets using Python libraries.

Student Effort Hours: 
Student Effort Type Hours
Autonomous Student Learning








Approaches to Teaching and Learning:
Learning theoretical concepts in lectures.
Learning practical skills through assignments. 
Requirements, Exclusions and Recommendations

Not applicable to this module.

Module Requisites and Incompatibles
COMP30030 - Introduction to AI, COMP47490 - Machine Learning (UG)

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Machine Learning Exercise Week 9 n/a Alternative linear conversion grade scale 40% No


Examination: End of semester exam 1 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No


Assignment: Machine Learning exercise Week 6 n/a Alternative linear conversion grade scale 40% No


Carry forward of passed components
Resit In Terminal Exam
Spring 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

How will my Feedback be Delivered?

Not yet recorded.

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) - Autumn: All Weeks Thurs 10:00 - 10:50
Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 12:00 - 12:50