Module Catalogue 2024/25

Pre Requisite Comment

N/A

Co Requisite Comment

N/A

Aims

This module aims to provide a foundation in the field data analytics, predictive data science, computer vision methods and machine learning.

Outline Of Syllabus

Selected topics chosen from:

Principles and practice of computer vision:
Background, Image Model, Spatial Coordinate, Digitisation.
Image Sampling, Image Quality, Image Pixel Relationships.
Linear Operators, 2-D Transforms. Spatial Domain Methods, Frequency Domain Methods.
Image Compression, Lossless and Lossy Compression, Compression Standards.
Object Detection Methods, Edge Liking and Boundary Detection.
Thresholding Methods, Region Oriented Methods. Pattern Recognition, Segmentation, Introduction to Convolutional Neural Network (CNN).


Principles and practice of machine learning and deep neural networks:
Covering core concepts, and techniques along with current leading approaches in machine/deep learning.

Intended Knowledge Outcomes

After completing this module students will be able to articulate an understanding of:

- Fundamental techniques as well as Industry-grade data management infrastructure for data analytics
- Image processing and computer vision methods for image informatics applications.
- Machine learning and deep learning at a foundational level.
- Approaches to choosing the most appropriate techniques for high quality data analytics

Intended Skill Outcomes

After completing this module students will have be able to:
-       Clean, process and analyse data.
-       Choose and apply image processing and computer vision methods.
-       Choose and apply machine learning and deep learning techniques.

Teaching Rationale And Relationship

The teaching methods combine traditional lectures with practical sessions so that students can explore the topics covered in both a theoretical and practical context. Lectures outline the underlying principles, algorithms and theory, while practical lab work encourages students to implement the algorithms using rea-world data, in terms of applying the methods to real world data examples.
Lecture material will be pre-recorded and students have the opportunity to watch the videos ahead of the lecture. Lectures in person and where possible streamed live with recap will be available. Lecture follow up will consist of Q&A about the lecture material.

Assessment Rationale And Relationship

The assessment is based on case studies, using real world data, allowing students to explore practical application of the techniques and algorithms that have been learned. The reflective report offers students the opportunity to draw together the overall learning experience on machine learning and predictive analysis of data sets.

General Notes

N/A