Module Details

Module Code: COMP9067
Title: Deep Learning
Long Title: Deep Learning
NFQ Level: Expert
Valid From: Semester 1 - 2018/19 ( September 2018 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 4 programme(s)
Module Description: Deep learning techniques, which are a subfield of machine learning, has led to significant advances in challenging real-world problems such as natural language processing and image recognition. This module focuses on equipping students with both the theoretical and practical skills that will enable them to build and apply deep learning models to real-world problems.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Implement and evaluate a gradient descent-based machine learning algorithm.
LO2 Build, train and apply deep neural networks to problems such as computer vision.
LO3 Perform hyperparameter optimization, regularization and optimization for deep learning networks.
LO4 Create convolutional neural network models and apply to image datasets.
LO5 Build and train Recurrent Neural Networks (RNNs).
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Regression and Gradient Descent.
Introduction to linear regression and gradient descent. Multiple linear regression and metrics for evaluating regression models. Logistic regression and activation functions. Using a vectorized implementation.
Build and evaluate deep neural networks.
Build and train shallow neural networks. Forward and backward propagation. Key parameters for neural networks. Create and train a fully connect deep learning model. Initialization, L2 and dropout regularization, gradient checking and batch normalization. Convergence algorithms. Best-practice for evaluating performance and analyzing for bias and variance.
Convolutional neural network.
Overview of convolutional neural networks. Methodology for stacking layers in a deep network to address multi-class image classification problems. Object detection and the YOLO algorithm. Deep residual learning for image recognition.
Recurrent Neural Networks (RNNs).
The basic recurrent unit (Elman unit) and LSTM (long short-term memory) unit. Overview of the GRU (gated recurrent unit). Build and train recurrent neural networks. Approaches for mitigating the vanishing gradient problem.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 7 Learning Outcomes 1,2,3
Assessment Description
Perform a comparative analysis between a basic gradient descent-based machine learning model and a deep learning neural network applied to a dataset from a specific application domain.
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 4,5
Assessment Description
Build and train a convolutional or recurrent neural network and apply to a dataset from a specific application domain. A comprehensive evaluation should be completed.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student undertakes independent study. The student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • I. Goodfellow , Y. Bengio, A. Courville. (2017), Deep Learning (Adaptive Computation and Machine Learning series), 1st. MIT Press, [ISBN: 9780262035613].
Supplementary Book Resources
  • T. Laville. (2017), Deep Learning for Beginners: Concepts, Techniques and Tools, 1st. CreateSpace Independent Publishing, [ISBN: 9781979311182].
  • F. Chollet. (2017), Deep Learning with Python, 1st. Manning Publications, [ISBN: 9781617294433].
Recommended Article/Paper Resources
  • S. Ioffe, C. Szegedy. (2015), Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, International Conference on Machine Learning.
  • K. He, X. Zhang, S. Ren, J. Sun. (2016), Deep Residual Learning for Image Recognition, IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
Other Resources
  • Website, TensorFlow. https://www.tensorflow.org/.
  • Website, Theano. http://deeplearning.net/software/theano/.
  • Website, Caffee. http://caffe.berkeleyvision.org/.
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_KARIN_9 Master of Science in Artificial Intelligence 2 Mandatory
CR_SCOBI_9 Master of Science in Computational Biology 4 Elective
CR_SNUHA_9 Master of Science in Nutrition & Health Analytics 4 Elective
CR_SCPBI_9 Postgraduate Diploma in Science in Computational Biology 4 Elective