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COMP3027J

Academic Year 2024/2025

Data Mining and Machine Learning (COMP3027J)

Subject:
Computer Science
College:
Science
School:
Computer Science
Level:
3 (Degree)
Credits:
5
Module Coordinator:
Dr Ruihai Dong
Trimester:
Spring
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades
Campus of Delivery:
BDIC(UCD) Beijing

Curricular information is subject to change.

The objective of this module is to familiarise students with the fundamental theoretical concepts in data mining and machine learning, as well as to instruct students in the practical aspects of applying data mining and machine learning algorithms. Key techniques in supervised machine learning will be covered, such as classification algorithms, and regression analysis. A particular emphasis will be placed on the evaluation of the performance of these algorithms. Further topics covered include such as classical neural networks.

About this Module

Learning Outcomes:

On completion of this module, students will be able to:
1) Distinguish between the different categories of data mining and machine learning algorithms;
2) Identify a suitable data mining/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
Lectures

22
Practical

20
Autonomous Student Learning

83
Total

125

Approaches to Teaching and Learning:
The key teaching and learning approaches used in the module include: active/task-based learning; lectures; lab work; enquiry & problem-based learning.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Required:
BDIC1047J - English for Uni Studies BDIC, BDIC1048J - English Gen Acad Purposes BDIC, BDIC2007J - English for Spec Acad Purposes, BDIC2015J - Acad Wrt & Comm Skills

Additional Information:
This module is delivered overseas and is not available to students based at the UCD Belfield or UCD Blackrock campuses.


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): The purpose of this assignment is to practice how to use data mining and machine learning to solve real-world problems. Week 4, Week 5, Week 6, Week 7, Week 8 Standard conversion grade scale 40% No
40
 No
Assignment(Including Essay): The purpose of this assignment is to practice how to use data mining and machine learning to solve real-world problems. Week 9, Week 10, Week 11, Week 12, Week 14 Standard conversion grade scale 40% No
60
 No

Carry forward of passed components
No
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Varies throughout the Trimester

Name Role
Ms Yu An Tutor
Ms Jie Chen Tutor
Xiao Li Tutor
Dairui Liu Tutor
Ms Qin Ruan Tutor
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