Registry
Module Specifications
Archived Version 2019 - 2020
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Description Deep learning is an emerging branch of machine learning that focuses on learning appropriate data representations from the data itself rather than operating with respect to a predefined model. In this course we will explore the fundamental elements and models of deep learning used for financial applications. The latter include portfolio management, social & news analysis as well as automated extraction of information from other non-financial sources in order to inform sophisticated AI-based financial predictions. We will start with fundamental machine kerning methods, including logistic regression, ensemble and classification techniques. Then the basics of deep learning will be introduced to demonstrate the construction of simple feed-forward and convolutional neural networks. We will address the special type of deep learning architectures that is suitable for time series analysis: recurrent neural networks (RNNs), and in particular, a special type of RNNs: long short-term memory (LSTM) networks. The presentation will focus on financial applications and practical understanding of the critical deep learning concepts such as generalization, overfitting and model architecture. The course will include labs and rely on the use of Python programming language for Keras / PyTorch implementations of neural networks. | |||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Model Design 2. Model Training 3. Model Testing | |||||||||||||||||||||||||||||||||||||
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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Other Resources None | |||||||||||||||||||||||||||||||||||||
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