COMP3027J Data Mining and Machine Learning

Academic Year 2021/2022

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.

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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 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

24
Practical

24
Autonomous Student Learning

75
Total

123
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:
BDIC1034J - College English 1, BDIC1035J - College English 2, BDIC1036J - College English 3, BDIC1037J - College English 4, BDIC1047J - English for Uni Studies BDIC, BDIC1048J - English Gen Acad Purposes BDIC, BDIC2007J - English for Spec Acad Purposes, BDIC2015J - Acad Wrt & Comm Skills


 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Assignment: Assignment Varies over the Trimester n/a Graded No

100

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
Xiao Li Tutor
Dairui Liu Tutor
Ms Qin Ruan Tutor