Module Code - Title:

CE4317 - INTRODUCTION TO DATA ENGINEERING AND MACHINE LEARNING

Year Last Offered:

2025/6

Hours Per Week:

Lecture

0

Lab

2

Tutorial

2

Other

0

Private

6

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

To give undergraduate students an insight and grounding into information/data engineering and machine learning. The module will cover mathematical and coding skills essential to developing machine learning applications in Python and will provide an introduction to some advanced machine learning topics such as modern machine learning platforms, data visualisation, and deep learning.

Syllabus:

Students undertaking this module will undertake learning in: a programming language (e.g. Python) for machine learning; numeric support in typical scientific scripting (e.g., Numpy/Scipy); graphics and Scientific Visualization: Using scripting languages to build scientific visualizations (Matplotlib); fundamentals and basic concepts of machine learning algorithms (Perceptron, Logistic Regression, Support Vector Machines, Multi-Layer Perceptron); programming basics for machine learning (Scikitlearn, Pandas); and, applications of machine learning (e.g., netwrok intrusion detection, etc)

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

On successful completion of this module students will be able to: 1. Solve a simple machine learning problem. 2. Demonstrate an understanding, select and apply appropriate machine learning approaches from those presented in the module. 3. Demonstrate an understanding, select and apply suitable data visualisation techniques. 4. Demonstrate an understanding, select and apply suitable machine learning algorithms to investigate and identify trends in data.

Affective (Attitudes and Values)

On successful completion of this module students will be able to: 1. Demonstrate an understanding of potential biases potential biases in given datasets 2. Following exposure to various frameworks and hosted platforms, the student will be able to judge and challenge the limitations of current machine learning techniques. 3. Demonstrate an understanding that there is no single machine learner that is best in all cases (the so-called 'No Free Lunch Theorem').

Psychomotor (Physical Skills)

N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

The module will be delivered using a blended learning approach with online aspects as well as face-to-face laboratory interaction. The content is divided into two-week activities with a submission at the end of every two-week window.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Sebastian Raschka & Vahid Mirhjalili (2017) Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition , Packt Publishing

Andreas C. Müller and Sarah Guido (2016) Introduction to Machine Learning with Python: A Guide for Data Scientists , O'Reilly

Erwin Kreyszig (2006) ADVANCED ENGINEERING MATHEMATICS , Wiley

Other Relevant Texts:

Brian K. Jones and David M. Beazley (2011) Python Cookbook: Recipes for Mastering Python 3 , O'Reilly

Programme(s) in which this Module is Offered:

BSCSIFUFA - CYBER SECURITY AND IT FORENSICS

Semester(s) Module is Offered:

Autumn

Module Leader:

ciaran.eising@ul.ie