Registry
Module Specifications
Archived Version 2021 - 2022
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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 the first half of this course we will explore the fundamental techniques and approaches of machine learning that led to the development and success of deep learning. These include stochastic gradient decent, random forests, perceptron, support vector machines and naïve Bayes methods. After that artificial neural networks and the basics of deep learning will be introduced to demonstrate the construction of simple feed-forward and more complex 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 actuarial and financial applications to ensure practical understanding of the critical machine learning and deep learning concepts such as representation, generalization, overfitting and model architecture. The lectures will be supported by computer labs demonstrating the use of the proposed methodologies using R / Keras programming language. | |||||||||||||||||||||||||||||||||||||
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Learning Outcomes 1. Solve linearly separable classification problems using trees and their collections 2. Construct kernel-based machine learning methods like Support Vector Machines 3. Apply generative models to performs probabilistic machine learning modeling 4. Apply perceptron and neural networks models 5. Build, train and deploy deep neural networks, RNNs, LSTMs | |||||||||||||||||||||||||||||||||||||
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 Linear models and gradient decent techniquesDecision trees and ensemble learningOnline learning: perceptron, support vector machinesGenerative models and naive Bayes methodsNeural networks and backpropagationDeep learning, RNNs, LSTMs | |||||||||||||||||||||||||||||||||||||
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Indicative Reading List | |||||||||||||||||||||||||||||||||||||
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Other Resources None | |||||||||||||||||||||||||||||||||||||
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