Module Code: H9MLFF
Long Title Machine Learning for Finance
Title Machine Learning for Finance
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Rohit Verma
Module Author: Andrea Del Campo Dugova
Departments: School of Computing
Specifications of the qualifications and experience required of staff

Lecturer    PhD/Master’s degree in a computing or cognate discipline. May have industry experience also.
Tutor    PhD/Master’s degree in a computing or cognate discipline. May have industry experience also.
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Retrieve, extract, manipulate, synthesise, explore, and visualise data in preparation for data analysis and machine learning.
LO2 Demonstrate expert knowledge of the theory, concepts and methods associated with the analysis of data using numerical and statistical techniques to assist on decision-making.
LO3 Use fundamental machine learning concepts and techniques to build and evaluate machine learning models on various problem domains.
LO4 Evaluate and employ graphical tools for building comprehensive analytics processes and dashboards.
LO5 Critically analyse, compare, summarise, and present results to support decision making and address requirements in real-world problems.
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).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Programme entry requirements must be satisfied. 
 

Module Content & Assessment

Indicative Content
Regression and Classification Algorithms I
Best-practice for evaluating performance and analysing for bias and variance. KNN and regression and classification.
Regression and Classification Algorithms II
Partial Least Squares Regression. Decision Tree regression and classification.
Regression and Classification Algorithms III
Support Vector Machines regression and classification.
Classification Algorithms
Logistic regression; Naïve-Bayes.
Ensembles
Random Forest; Voting; Stacking; Bagging and Boosting Methods.
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.
Deep Neural Networks II
Initialisation, L2 and dropout regularisation, gradient checking and batch and layer normalisation; convergence algorithms, learning rate scheduling, Hyperparameter tuning.
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.
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.
Transformer
Encoder-decoder Architecture, attention mechanisms, Position embedding, multi-head attention and self-attention layers, pre-trained language models (e.g., BERT)
Application of Deep Learning Models to Finance I
Selected topics from the following areas will be covered, especially on practical applications: computer vision, natural language processing
Application of Deep Learning Models to Finance II
Selected topics from the following areas will be covered, especially on practical applications: computer vision, natural language processing
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
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.
Assessment Type: Project % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
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.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
The repeat strategy for this module is by a project that covers all learning outcomes.

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 and demonstrations 24 Per Semester 2.00
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Independent Learning Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
Workload: Blended
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom and demonstrations 12 Per Semester 1.00
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Tutorial Directed e-learning 12 Every Week 12.00
Independent Learning Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 14.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 24 Per Semester 2.00
Tutorial Mentoring and small-group tutoring 12 Per Semester 1.00
Independent Learning Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
 

Module Resources

Recommended Book Resources
  • John D. Kelleher,Brian Mac Namee,Aoife D'Arcy. (2020), Fundamentals of Machine Learning for Predictive Data Analytics, second edition, MIT Press, p.853, [ISBN: 978-0262044691].
  • Ian Goodfellow,Yoshua Bengio,Aaron Courville. (2016), Deep Learning, MIT Press, p.801, [ISBN: 978-0262035613].
  • Hastie, T., Tibshirani, R., & Friedman, J.. The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2ND ED. Springer.
Supplementary Book Resources
  • Kevin P. Murphy. (2012), Machine Learning: A Probabilistic Perspective., MIT Press, p.1102, [ISBN: 978-0262018029].
  • Gareth James,Daniela Witten,Trevor Hastie,Robert Tibshirani. (2014), An Introduction to Statistical Learning, Springer, p.426, [ISBN: 978-1461471370].
  • Max Kuhn,Kjell Johnson. (2018), Applied Predictive Modeling, Springer, p.600, [ISBN: 978-1461468486].
  • Shai Shalev-Shwartz,Shai Ben-David. (2014), Understanding Machine Learning, Cambridge University Press, p.415, [ISBN: 978-1107057135].
  • John Hearty. (2016), Advanced Mastering Learning with Python, Packt Publishing, p.278, [ISBN: 978-1784398637].
This module does not have any article/paper resources
Other Resources
Discussion Note: