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COMP47460

Academic Year 2023/2024

Machine Learning (Blended Delivery) (COMP47460)

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

Curricular information is subject to change.

The objective of this module is to familiarise students with the fundamental theoretical concepts in machine learning, as well as instructing 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. 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 on real datasets using a standard machine learning toolkit.

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

80

Lectures

16

Tutorial

8

Practical

4

Total

108


Approaches to Teaching and Learning:
Recorded Audio Lectures
Tutorial/Review Sessions during the semester
Online discussion forums
Tutorials
Assignments

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
COMP30030 - Introduction to AI, COMP30120 - Intro to Machine Learning, COMP41450 - Advanced Machine Learning, COMP47490 - Machine Learning (UG), COMP47990 - Machine Learning w Python (OL)


 

Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment: Assignment 1 Unspecified n/a Graded No

20

No
Assignment: Assignment 2 Unspecified n/a Graded No

20

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

60

No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring Yes - 2 Hour
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.