Registry
Module Specifications
Archived Version 2021 - 2022
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Description This module develops knowledge of the techniques of machine learning to learn from financial data and build new types of financial models. Through the application of artificial neural networks, support vector machines, random forests, and gradient boosting, we will bring new understanding to financial models. The machine learning techniques are applied to build, for example, asset pricing models, credit acceptance / rejection models, and fraud detection models. There will also be coverage of the practical issues of setting up secure data infrastructures for implementing these techniques in the firm. A second focus will be on how to use machine learning, as well as other tools, to implement automation to financial services provision in the firm. The micro is delivered through the Python language. | |||||||||||||||||||||||||||||||||||||
Learning Outcomes 1. Demonstrate understanding of the key machine learning techniques of benefit to financial modeling 2. Apply machine learning techniques appropriately in a variety of practical financial context 3. Understand the data needs, and appropriate and secure data handling skills, for working with financial models 4. Implement automation to financial decision making based on financial machine learning and understanding of financial technology | |||||||||||||||||||||||||||||||||||||
All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml |
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Indicative Content and
Learning Activities Regression-based machine learningExploration of various techniques associated with regression. Including setting up data for testing, pre-processing. To cover techniques such as linear regression, gradient boosting, random forests, MLP. Techniques to be applied through sklearn in Python with finance case studies.Grouping-based machine learningExploration of various techniques associated with clustering and grouping. Starting with logistic regression, decision trees, SVM, deep learning clustering and grouping.Other machine learningExamining techniques such as outlier detection and its benefit for fraud detection and credit default risk in finance. Also examining (very briefly) the use of text-based machine learning models. Data security and data protection.Automation for FinTechTechniques and tools for implementing automation in financial technology. Examined from a robotic process automation perspective as well as a user experience perspective. Advice from expert practitioners on practical implementation. | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List
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Other Resources 45275, 0, DataCamp (various courses), www.datacamp.com, | |||||||||||||||||||||||||||||||||||||
Programme or List of Programmes | |||||||||||||||||||||||||||||||||||||
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