The course covers practical aspects of machine learning, and will focus on practical and experimental issues in deep learning and neural networks. Topics that are covered include:

* Feed-forward network architectures
* Optimisation and learning rules
* Regularisation and normalisation
* Neural networks for classification
* Autoencoders
* Convolutional Neural Networks
* Recurrent Neural Networks

* Transformers

MLP is coursework-based, with lectures to support the additional material required to carry out the practical. Students who complete this course will have experience in the design, implementation, training, and evaluation of machine learning systems.

MLP is a two-semester course. During Semester 1 the course will focus on developing a deep learning framework based on experiments using the task of classification of handwritten digits using the well-known MNIST dataset. The course uses a Python software framework, and a series of Jupyter notebooks. There is a series of ten weekly lectures in Semester 1 to provide the required theoretical support to the practical work.

Semester 2 will be based on small group projects, with a focus on using deep neural networks within the context of a miniproject, using an open source toolkit such as TensorFlow or PyTorch. Lectures in Semester 2 will support the coursework, and also provide insights to the current state of the art in this very fast moving area.