EE4146Data Engineering and Machine Learning

What you will study

Course overview and aims

This course aim is to introduce the concept, techniques and fundamental background to informatics and learning systems.

Keyword syllabus

Introduction to Data Engineering Understand data engineering methodologies, learning systems and its analysis; formulate an IT/business/engineering problem from the perspective of learning systems; understand fundamental concepts in data engineering and classification; recognize issues in practical data engineering and IT problems. Emerging Machine Learning Technology Introduction to Machine Learning Technology: Statistical methods such as Discriminant Analysis, and Principal Component Analysis; Supervised learning such as Naïve Bayes classifier, K Nearest Neighbour, and neural networks; Unsupervised learning approaches such as Self-Organizing Map, and Clustering; High Dimensionality Reduction such as linear discriminant analysis (LDA), manifold learning, and feature selection methods; Diagnostic Analysis, and Practical Case Study.

Official PDF, page 4 · Keyword Syllabus

Intended learning outcomes

View learning outcomes

CILO	Intended learning outcome	Weight	DEC
1	Describe the current learning systems and methodologies used in engineering, IT and data industries	—	DEC-A1, DEC-A2
2	Describe the use of supervised and unsupervised learning methods for engineering, IT and data engineering problems	—	DEC-A1, DEC-A2
3	Analyze practical data engineering and machine learning problems using supervised or unsupervised learning methods	—	DEC-A1, DEC-A2

Official PDF, page 2 · Course Intended Learning Outcomes (CILOs)

Before you take this course

Check prerequisites, precursors and corequisites separately, together with equivalence and exclusion conditions. “and” means conditions must be met together; “or” marks alternatives. Read combined conditions in the groupings shown in the original text below.

Prerequisites

(MA3001 Differential Equations or EE3121 Differential Equations for Electrical Engineering) or EE3001 Foundations of Data Engineering or (MA3160 Probability and Stochastic Processes or EE3331 Probability Models in Information Engineering)

Precursors

EE3211 Modelling Techniques

Corequisites

This edition of the official syllabus does not state this item.

Additional course requirements

This edition of the official syllabus does not state this item.

Equivalent courses and conditions

Nil

Exclusive courses and conditions

Nil

Assessment and passing the course

Assessment

Continuous assessment: 50%; Examination: 50%; Examination duration: 2 hours

Assessment breakdown

Assessment task	Weight	Generative AI	Remarks and restrictions
TestsCILO 1, 2, 3	35%	No	-
AssignmentsCILO 1, 2, 3	15%	Yes	-

Official PDF, page 3 · Assessment Tasks / Activities (ATs)

View the original assessment table and instructions

Tests (min.: 2) | CILO No. 1,2,3 | 35% | - | No
#Assignments (min.: 3) | CILO No. 1,2,3 | 15% | - | Yes

Passing requirements

Remarks: To pass the course, students are required to achieve at least 30% in course work and 30% in the examination. # may include homework, tutorial exercise, project/mini-project, presentation.

The catalogue assessment percentages are indicative; the course syllabus provides the detailed assessment scheme.