Module Code - Title:

ME4316 - INTRODUCTION TO MACHINE LEARNING FOR ENGINEERS

Year Last Offered:

N/A

Hours Per Week:

Lecture

2

Lab

2

Tutorial

1

Other

0

Private

5

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

The aim of the module to give 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:

1. Programming language (e.g. Python) for machine learning 2. Numeric support in typical scientific scripting (e.g., Numpy/Scipy). 3. Graphics and Scientific Visualization: Using scripting languages to build scientific visualizations. 4. Basic concepts of machine learning. 5. Programming basics for machine learning. 6. Introduction to machine learning frameworks (e.g. Scikitlearn)

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: Accept that there is no single machine learning algorithm 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 take a blended learning approach, using some online components in addition to face-to-face contact.

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