Module Code: |
H8NNPA |
Long Title
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Neural Networks & Prescriptive Analytics
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Title
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Neural Networks & Prescriptive Analytics
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Module Level: |
LEVEL 8 |
EQF Level: |
6 |
EHEA Level: |
First Cycle |
Module Coordinator: |
Isabel O'Connor |
Module Author: |
Isabel O'Connor |
Departments: |
School of Computing
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Specifications of the qualifications and experience required of staff |
Master’s degree or PhD in a computing or cognate discipline. May have industry experience also.
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Learning Outcomes |
On successful completion of this module the learner will be able to: |
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Learning Outcome Description |
LO1 |
Describe a range of different neural network models and identify specific applications |
LO2 |
Identify architectures and optimization methods for deep neural network training |
LO3 |
Implement deep learning methods and apply them to data using state of the art deep learning tools |
LO4 |
Explain and evaluate the basic underlying principles of heuristic search as optimization methods to solve complex problems |
LO5 |
Comprehend and apply the methodologies of the most commonly used heuristics (Greedy, Simulated Annealing, Tabu Search, Evolutionary algorithms, Ant Colony optimization) |
LO6 |
Develop new (hybrid) heuristic methods by extending and combining existing heuristic search strategies |
LO7 |
Apply heuristics algorithms to solve complex problems in real-world engineering and business scenarios using the state of the art software tools |
Dependencies |
Module Recommendations
This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
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No recommendations listed |
Co-requisite Modules
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No Co-requisite modules listed |
Entry requirements |
Learners should have attained the knowledge, skills and competence gained from stage 3 of the BSc (Hons) in Data Science
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Module Content & Assessment
Indicative Content |
Neural Network Representations and Forward Propagation
- From Linear to NonLinear Classifiers. - Units, Layers, Bias Units. - Building nonlinear functions. - The Feed Forward Algorithm
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Backpropagation
- Overview of Backpropagation Methods. - Deriving the Backpropagation Equations. - The Backpropagation Algorithm. - Visualizing Backpropogation in TensorFlow
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Refining Backpropagation
- Cross Entropy Loss function. - Hyperbolic Tangent Units. - Rectified Linear Units. - Softmax Layers
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Preventing Overfitting
- Regularization in Neural Networks. - Early Stopping. - Dropout
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Convolutional Neural Networks
- Convolutions. - Pooling Layers. - Implementing a CNN. - Scaling networks with a GPU
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Recurrent Neural Networks
- Basic topology. - Motivating examples. - Long Short Term Memory
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Common concepts for evolutionary methods
- Representation. - Objective function. - Constraint handling. - Performance analysis
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Single-solution based metaheuristics
- Fitness landscapes. - Local search. - Simulated annealing. - Tabu search. - Variable neighbourhood search
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Evolutionary algorithms
- Genetic algorithms. - Swarm intelligence. - Memetic algorithms swarm intelligence
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Hybrid metaheuristics
Combining metaheuristics with mathematical programming, constraint programming, machine learning and data mining
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Applications I
Analytical customer relationship management, Clinical decision support systems, Direct marketing, Fraud detection. Ethics implications
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Applications II
Project risk management, oil and natural gas exploration, logistics and transportation. Ethics implications
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Assessment Breakdown | % |
Coursework | 70.00% |
End of Module Assessment | 30.00% |
AssessmentsFull Time
Coursework |
Assessment Type: |
Continuous Assessment |
% of total: |
Non-Marked |
Assessment Date: |
n/a |
Outcome addressed: |
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Non-Marked: |
Yes |
Assessment Description: Ongoing independent and group problem solving activities and feedback. |
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Assessment Type: |
Project |
% of total: |
70 |
Assessment Date: |
n/a |
Outcome addressed: |
3,6 |
Non-Marked: |
No |
Assessment Description: Long-form project which the student produces over the course of the entire semester. Student is required to choose to develop an application in predictive analytics or prescriptive analytics using deep learning or evolutionary techniques. |
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End of Module Assessment |
Assessment Type: |
Terminal Exam |
% of total: |
30 |
Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4,5,6,7 |
Non-Marked: |
No |
Assessment Description: Terminal assessment exam taken over 2 hours with four questions of which the student must answer three to address the students' understanding of the underlying theories and concepts |
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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.
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Reassessment Description The repeat strategy for this module is an examination. All learning outcomes will be assessed in the repeat exam.
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NCIRL reserves the right to alter the nature and timings of assessment
Module Workload
Module Target Workload Hours 0 Hours |
Workload: Full Time |
Workload Type |
Workload Description |
Hours |
Frequency |
Average Weekly Learner Workload |
Lecture |
Classroom & Demonstrations (hours) |
24 |
Per Semester |
2.00 |
Tutorial |
Other hours (Practical/Tutorial) |
24 |
Per Semester |
2.00 |
Independent Learning |
Independent learning (hours) |
202 |
Per Semester |
16.83 |
Total Weekly Contact Hours |
4.00 |
Module Resources
Recommended Book Resources |
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Goodfellow, I., Bengio, Y. & Courville, A.. (2016), , Deep Learning, MIT Press.
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Simon, D.. (2013), Evolutionary Optimization Algorithms, Wiley.
| Supplementary Book Resources |
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Buduma, N.. (2017), Fundamentals of Deep Learning: Designing Next-Generation Machine Intelligence Algorithms, O'Reilly Media.
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Chang Wook, A.. (2006), Advances in Evolutionary Algorithms: Theory, Design and Practice (Studies in Computational Intelligence), Springer-Verlag.
| This module does not have any article/paper resources |
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This module does not have any other resources |
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