Module Catalogues, Xi'an Jiaotong-Liverpool University   
Module Code: CSE414
Module Title: Data Mining and Machine Learning
Module Level: Level 4
Module Credits: 5.00
Academic Year: 2019/20
Semester: SEM2
Originating Department: Computer Science and Software Engineering
Pre-requisites: N/A
To equip students with a comprehensive knowledge on the principles, techniques, algorithms and applications of Machine Learning (ML) and Data Mining (DM).

This module provides analysis techniques for today’s pervasive data, which are fundamental for business and financial analytics practitioners.
Learning outcomes 
A. Demonstrate a basic and advanced knowledge of various Data Mining (DM) and Machine Learning (ML) algorithms from a practitioner/user perspective.

B. Critically assess the strengths and weaknesses of various DM and ML algorithms.

C. Apply DM and ML algorithms using a suitable (e.g. the Weka or R) platform.

D. Identify, formulate and solve problems arising from practical applications using DM and ML principles and techniques.

Method of teaching and learning 
Students are expected to attend a two- hour formal lecture and either a two-hour tutorial or a two-hour lab session in a typical week. Lectures deliver the contents specified in the syllabus. Tutorials/labs expand students’ understanding of lecture materials and equip them with necessary programming skills.

In addition, students are expected to devote the required number of hours as unsupervised / private studies for reflection of lecture materials and reading and practical work. Two practical coursework assignments are used to assess their understanding of the lecture materials and their capability in solving practical problems. A written examination at the end of the module assesses their overall academic achievement.

Week 1: Overview of data mining and machine learning.

Week 2: Measurement and data mining process: e.g. nature of datasets, types of measurement, distance measures, transforming data, data quality, models and patterns of data mining process.

Weeks 3-4: Mathematics and programming languages: e.g. probability, calculus, basic commands, graphics, indexing data, loading data, matrix calculation, statistics, installing packages, packages related to regression, dimensionality reduction, associate rule mining, and clustering.

Week 5: Regression: linear regression, multiple regression, logistic regression, examples and advanced applications.

Week 6: Dimensionality reduction: e.g. subsect selection, principal components analysis, linear discriminant analysis, examples and advanced applications.

Week 7-9 Classification: e.g. decision tree, ID3, C4.0, SVM, examples and advanced applications.

Week 10-12 Clustering: e.g. k-means, Hierarchical clustering, SOM, examples and advanced applications.

Week 13: Revision.

Delivery Hours  
Lectures Seminars Tutorials Lab/Prcaticals Fieldwork / Placement Other(Private study) Total
Hours/Semester 26    13  13    98  150 


Sequence Method % of Final Mark
1 Coursework 15.00
2 Coursework 15.00
3 Coursework 70.00

Module Catalogue generated from SITS CUT-OFF: 8/10/2020 4:12:33 AM