Module Details

Module Code: SYST C4606
Module Title: Deep Learning
Title: Deep Learning
Module Level:: 8
Credits:: 5
Module Coordinator: Frances Hardiman
Module Author:: James Garland
Domains:  
Module Description: Deep neural networks can inform both the contents of an image or video frame and the content's location within the image boundaries. Additionally, neural networks can manipulate images and video frames. This module investigates methods of image classification, location, and manipulation. The module also examines optimisation of the computation and storage of these neural network models' immense data to provide the student with a demonstrable understanding of the advanced neural network features.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Design AI modules that identify features in images.
LO2 Develop AI modules to track the movement of features in images.
LO3 Manage image manipulation within image sets, e.g., using GANs.
LO4 Improve the performance of the neural network model.
LO5 Complete a project as an individual or in a small group to design and implement a solution for a real world problem.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is recommended before enrolment in this module.
9271 COMP C4602 Computer Vision
9655 ELEC C4602 Artificial Intelligence and Machine Learning
Co-requisite Modules
No Co-requisite modules listed
Additional Requisite Information
No Co Requisites listed
 
Indicative Content
Image classification and localisation
Models to find the best classification accuracy and localisation of images. Localisation of objects in an image or video stream.
Semantic segmentation
Location and movement of items within a frame.
Image Manipulation
Generative variational auto-encoders, generative adversarial Networks (GANs), spectral normalisation.
Optimisation
Optimisation techniques such as pruning, activation functions, compression, and alternative number representation.
Ethics, Safety, and Trustworthiness
Algorithm and data bias, model safety and EU trustworthiness policy, GDPR considerations.
Module Content & Assessment
Assessment Breakdown%
Continuous Assessment20.00%
Project40.00%
Practical40.00%

Assessments

Full Time

Continuous Assessment
Assessment Type Short Answer Questions % of Total Mark 20
Timing Week 4 Learning Outcomes 1,2
Non-marked No
Assessment Description
n/a
Project
Assessment Type Project % of Total Mark 40
Timing Sem 2 End Learning Outcomes 1,2,3,4,5
Non-marked No
Assessment Description
n/a
Practical
Assessment Type Practical/Skills Evaluation % of Total Mark 40
Timing Every Week Learning Outcomes 1,2,3,4
Non-marked No
Assessment Description
n/a
No End of Module Formal Examination
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

SETU Carlow Campus reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Category Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact No Description Every Week 2.00 2
Laboratory Contact No Description Every Week 3.00 3
Independent Learning Non Contact No Description Every Week 6.00 6
Total Weekly Contact Hours 5.00
 
Module Resources
Recommended Book Resources
  • Géron, A.. (2019), Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems., O'Reilly Media.
  • Goodfellow, I., Bengio, Y., Courville, A. (2016), Deep learning, 2. Cambridge: MIT press.
  • Bishop, C. (2006), Pattern Recognition and Machine Learning, Springer.
Recommended Article/Paper Resources
  • Dufresne-Camaro, C. O., Chevalier, F., & Ahmed, S. I.. (2020), Computer vision applications and their ethical risks in the global south., Canadian Human-Computer Communications Society (CHCCS), p.10,
    https://openreview.net/pdf?id=QLFSDNIvI
Other Resources
Discussion Note: