This module is not currently running in 2024 to 2025.
Deep learning is a modern branch of machine learning that is entirely based on artificial neural networks. Decision-making based on deep learning algorithms is of great importance in many fields. In this module, students study deep learning models (including the algorithms in deep learning model training) and the use of deep learning and its applications in decision making. Both theoretical and practical aspects are covered, including the use of python.
Indicative content: Introduction to Python; perceptron; neural networks; feed-forward neural networks (FFNN); deep neural networks (DNN); convolutional neural networks (CNN); recurrent neural networks (RNN); backpropagation algorithm; stochastic gradient descent (SGD) algorithm; applications of deep learning in decision making.
Total contact hours: 44
Private study hours: 106
Total study hours: 150
Level 6
Assessment 1 (10-15 hrs) 20%
Assessment 2 (10-15 hrs) 20%
Examination computer based open-book (2 hours) 60%
Reassessment methods
Like-for-like
Francois Chollet (2017), Deep Learning with Python. Manning, Shelter Island.
Sandro Skansi (2019) Introduction to Deep Learning. Springer, New York.
Gulli A., Pal S. (2017) Deep Learning with Keras. Packt Publishing. Birminham.
Goodfellow, I., Bengio, Y., Courville, A. (2016). Deep Learning. MIT Press, Cambridge.
1.demonstrate systematic understanding of key aspects of deep learning;
2.demonstrate the capability to deploy established approaches accurately to analyse and solve problems using a reasonable level of skill in calculation and manipulation of the material in the following areas: deep neural network models and learning algorithms;
3.apply key aspects of deep learning in well-defined contexts, showing judgement in the selection and application of tools and techniques;
4.show judgement in the selection and application of deep learning models and software.
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