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COMP47490

Academic Year 2024/2025

Machine Learning (UG) (COMP47490)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Vivek Nallur
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

Note that from 2021/22, this module is primarily meant for UG students.
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 and applications of machine learning will also be introduced. COMP47490 requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.

About this Module

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 using various evaluation metrics; 4) be able to use Python and scikit-learn for machine learning tasks using real datasets.

Indicative Module Content:

Classification Techniques including kNN, Decision Trees, Naive Bayes and SVM
Regression
Gradient Descent and Neural Networks
Ensembles
Evaluation Methodology and Measures
Introduction to Reinforcement Learning
Unsupervised learning techniques including dimensionality reduction, partitional and hierarchical clustering
Running machine learning tasks using Python/Scikit-learn

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Tutorial

24
Autonomous Student Learning

80
Total

128

Approaches to Teaching and Learning:
Practical Labs; Continuous Assessment; enquiry & problem-based learning;

Requirements, Exclusions and Recommendations
Learning Recommendations:

This module requires strong mathematical ability, as some of the algorithms require some understanding of linear algebra and statistical concepts.


Module Requisites and Incompatibles
Pre-requisite:
COMP30030 - Introduction to AI

Incompatibles:
COMP30120 - Intro to Machine Learning, COMP47460 - Machine Learning (Blended Del), COMP47750 - Machine Learning with Python, COMP47990 - Machine Learning w Python (OL)


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Quizzes/Short Exercises: Online quizzes conducted during labs Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11 Alternative linear conversion grade scale 40% No
70
 No
Individual Project: A coding project that requires dataset comprehension, coding and analysis. Week 11, Week 12 Alternative linear conversion grade scale 40% No
30
 No

Carry forward of passed components
No
 

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

How will my Feedback be Delivered?

Not yet recorded.

"Machine Learning" by Tom Mitchell
"Fundamentals of Machine Learning for Predictive Data Analytics" by John D. Kelleher, Brian Mac Namee, Aoife D'Arcy
"Machine Learning: The Art and Science of Algorithms that Make Sense of Data" by Peter Flach
"The Elements of Statistical Learning: Data Mining, Inference and Prediction" by Trevor Hastie, Robert Tibshirani and Jerome Friedman

Name Role
Dr Deepak Ajwani Lecturer / Co-Lecturer
Priscilla Adong Tutor

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 1, 2, 3, 4, 5, 6, 7, 9, 10, 11, 12 Mon 13:00 - 13:50
Autumn Practical Offering 1 Week(s) - Autumn: Weeks 2-12 Thurs 14:00 - 15:50
Autumn Lecture Offering 1 Week(s) - Autumn: All Weeks Wed 12:00 - 12:50
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