CS987 - Advanced Machine Learning For Data Analytics

Aims and Objectives

The aim of this class is to equip students with a sound understanding of the principles of neural networks and deep learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The class balances a solid theoretical knowledge of the techniques with practical application via Python (and associated libraries) and students are expected to be familiar with the language. Aspects of the course will be highly mathematical and technical requiring strong math and programming ability (Python and Pytorch).

Learning Outcomes

After completing this class participants will be able to:

• understand the aims and fundamental principles of neural networks and deep learning;
• understand a range of the essential core algorithms and approaches to neural networks and deep learning;
• apply the algorithms covered on substantial data sets using Python, Pytorch and Scikit-learn and interpret the outcomes;
• understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations;
• understand the limitations of basic approaches to neural networks and deep learning and the need for advanced strategies;
• understand and apply a range of the advanced algorithms and approaches to neural networks and deep learning and interpret the outcomes

Syllabus

Part 2: Artificial Neural Networks

• Perceptrons and Artificial Neural Networks
• Backpropagation and Network Training
• Multi-layer and Deep Neural Networks
• Convolutional Neural Networks
• Recurrent Neural Networks and Transformers 

Recommended Reading

This list is indicative only – the class lecturer may recommend alternative reading material. Please do not purchase any of the reading material listed below until you have confirmed with the class lecturer that it will be used for this class.

Charu C. Aggarwal, Neural Networks and Deep Learning, Springer Nature Link

Eli Stevens, Luca Antiga, Deep Learning with PyTorch, Manning

Last updated: 2025-04-28 11:14:37