Module Code: |
H9MLAI |
Long Title
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Machine Learning
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Title
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Machine Learning
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Module Level: |
LEVEL 9 |
EQF Level: |
7 |
EHEA Level: |
Second Cycle |
Module Coordinator: |
Rejwanul Haque |
Module Author: |
Shauni Hegarty |
Departments: |
School of Computing
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Specifications of the qualifications and experience required of staff |
PhD/Master’s degree 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 |
Select, apply, and evaluate machine learning methodologies to facilitate pre-processing and transformation approaches. |
LO2 |
Select, formulate, design, implement, and evaluate machine learning algorithms for solving real-world problems using the latest industry practices and standards. |
LO3 |
Contextualise, investigate, evaluate, and communicate key concepts and advanced techniques for machine and deep learning algorithms. |
LO4 |
Demonstrate expert knowledge of machine learning algorithms, tools, techniques utilised in real world contexts. |
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 |
Applicants are required to hold a minimum of a Level 8 honours qualification (2.2 or higher) or equivalent on the National Qualifications Framework in either STEM (e.g., Information Management Systems, Information Technologies, Computer Science, Computer Engineer) or Business (e.g., Business Information Systems, Business Administration, Economics) discipline and a minimum of three years of relevant work experience in industry, ideally but not necessarily, in management. Previous numerical and computer proficiencies should be part of their work experience or formal training. Graduates from disciplines which do not have technical or mathematical problem-solving skills embedded in their programme will need to be able to demonstrate technical or mathematical problem-solving skills in addition to their level 8 programme qualifications (Certifications, Additional Qualifications, Certified Experience and Assessment Tests). All applicants for the programme must provide evidence that they have prior Mathematics and Computing module experience (e.g., via academic transcripts or recognised certification) as demonstrated in one mathematics/statistics module and one computing module or statement of purpose must specify numerical and computing work experience.
NCI also operates a prior experiential learning policy where graduates with lower, or no formal qualifications, currently working in a relevant field, may be considered for the programme.
Applicants must also be able to have their own laptop with the minimum required specification that will be communicated to each applicant through both the admissions and marketing departments.
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Module Content & Assessment
Indicative Content |
Advanced Regression Models
Regression revision; Best-practice for evaluating performance and analysing for bias and variance, regularisation and penalised models, Generalised Linear Modelling
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Advanced Classification Models
Logistic regression; Naïve-Bayes Classification and Linear discriminant analysis
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Ensembles
Neural networks, classic topologies, and activation functions. Forward- and back-propagation. Optimisation algorithms: gradient descent and stochastic gradient descent. Key parameters for neural networks. Multi-layer perceptrons.
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Deep Neural Networks I
Neural networks, classic topologies, and activation functions. Forward- and back-propagation. Optimisation algorithms: gradient descent and stochastic gradient descent. Key parameters for neural networks. Multi-layer perceptrons.
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Deep Neural Networks II
Initialisation, L2 and dropout regularisation, gradient checking and batch and layer normalisation; convergence algorithms, learning rate scheduling, Hyperparameter tuning.
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Convolutional neural network
Overview of convolutional neural networks (CNN). 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.
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Recurrent Neural Network
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.
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Transformer
Encoder-decoder Architecture, attention mechanisms, Position embedding, multi-head attention and self-attention layers, pre-trained language models (e.g., BERT)
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Autoencoders and Deep Generative Models
Auto-encoders (AEs), Undercomplete, Regularised, Sparse autoencoders; Denoising autoencoders; Variational Autoencoder, Generative adversarial networks (GANs), Min-max cost, Conditional GAN
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Active learning (AL); Transfer learning (TL); Practical applications of AL and TL
Sampling techniques for stream-based and pool-based active learning; transfer learning strategies, multi-task and zero-shot learning, pre-trained models;
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Application of Deep Learning Models
Selected topics from the following areas will be covered, especially on practical applications: computer vision, natural language processing
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Application of Deep Learning Models
Selected topics from the following areas will be covered, especially on practical applications: computer vision, natural language processing
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Assessment Breakdown | % |
Coursework | 100.00% |
AssessmentsFull Time
Coursework |
Assessment Type: |
Formative Assessment |
% of total: |
Non-Marked |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
Non-Marked: |
Yes |
Assessment Description: Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning. |
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Assessment Type: |
Project |
% of total: |
100 |
Assessment Date: |
n/a |
Outcome addressed: |
1,2,3,4 |
Non-Marked: |
No |
Assessment Description: The terminal assessment will consist of a project that will evaluate all learning outcomes. The long-form project which the student produces over the course of the entire semester.
Learners will propose and execute an applied research project using appropriate machine learning methods, and critically compare their performance using appropriate evaluation metrics. The proposal should explain the background and context of the investigation with the topic or hypotheses that the learner proposes to investigate. The final submission will consist of a written report that demonstrates data sets used, design and implementation process of the machine learning models, results, discussions, and error analysis. |
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No End of Module Assessment |
Reassessment Requirement |
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
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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 |
Lectures |
24 |
Per Semester |
2.00 |
Independent Learning Time |
Independent Learning |
202 |
Per Semester |
16.83 |
Practical |
Practical/tutorials |
24 |
Per Semester |
2.00 |
Total Weekly Contact Hours |
4.00 |
Module Resources
Recommended Book Resources |
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Ian Goodfellow,Yoshua Bengio,Aaron Courville. (2016), Deep Learning, MIT Press, p.800, [ISBN: 978-0262035613].
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Hastie, T., Tibshirani, R., & Friedman, J.. (2016), The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd. Springer, [ISBN: 978-0387848570].
| Supplementary Book Resources |
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Kevin P. Murphy. (2012), Machine Learning, MIT Press, p.1104, [ISBN: 978-0262018029].
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Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani. (2014), An Introduction to Statistical Learning, Springer, p.426, [ISBN: 978-1461471370].
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Max Kuhn,Kjell Johnson. (2018), Applied Predictive Modeling, Springer, p.600, [ISBN: 978-1461468486].
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Shai Shalev-Shwartz,Shai Ben-David. (2014), Understanding Machine Learning, Cambridge University Press, p.409, [ISBN: 978-1107057135].
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John D. Kelleher,Brian Mac Namee,Aoife D'Arcy. (2020), Fundamentals of Machine Learning for Predictive Data Analytics, second edition, MIT Press, p.856, [ISBN: 978-0262044691].
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John Hearty. (2016), Advanced Mastering Learning with Python, Packt Publishing, p.278, [ISBN: 978-1784398637].
| This module does not have any article/paper resources |
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Other Resources |
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[Website], Machine Learning Stanford,
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[Website], DataCamp,
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[Website], UCI Repository,
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[Website], WEKA,
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[Website], Kaggle Competitions,
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