Module COMP53715: Deep Learning

Department: Computer Science

COMP53715: Deep Learning

Tied To

• G5T609: Advanced Computer Science
• G5T709: Advanced Computer Science (Artificial Intelligence)

Pre-requisites

Corequisites

Excluded Combinations

Aims

• To enable students to design solutions that learn to solve difficult high-dimensional problems across a variety of tasks.
• To position students to understand much of the current literature on deep learning, and be able to extend the knowledge with future study.

Content

• Foundations of machine learning and deep learning: linear regression, Bayesian infer-ence, bias/variance, generalisation, regularisation, cross-validation.
• PyTorch tensor programming and gradient descent.
• Designing architectures: feedforward neural networks, convolutional neural networks, residual architectures, transformers and attention.
• Modelling approaches: sequential models, generative adversarial networks, variational autoencoders, diffusion models, flow models.
• Training and optimisation on GPU servers, model evaluation and inference.

Learning Outcomes

• By the end of this module, students should be able to demonstrate:
• an understanding of the key principles of deep learning for use in curating datasets, designing, training and evaluating models.
• a critical understanding of the foundational approaches within current deep learning literature, neural network architectures and neural network architecture components.
• a systematic understanding of statistical methods and techniques used in machine learning and of statistical learning theory with respect to deep learning approaches.

• By the end of this module, students should be able to demonstrate:
• the scientific approach to design, training, validation, and testing of deep neural net-works using modern deep learning libraries in a broad range of applications.
• an ability to understand and identify inherent issues in dataset and algorithmic bias prior to training or architecture design.
• the ability to design efficient and bespoke neural networks with respect to the task requirements and dataset characteristics.

• By the end of this module, students should be able to demonstrate:
• an ability to critically evaluate and analyse complex problems according to the data structure and characteristics.
• an ability to understand the high-level theory and effectively communicate technical in-formation associated with deep learning literature.
• an ability to utilise modern GPU servers for learning to solve difficult high-dimensional problems.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• Lectures enable the students to learn new material relevant to machine and deep learning, as well as their applications.
• Computer classes enable students to acquire necessary coding skills, learn about the relevant libraries and packages and receive feedback on their work.
• The summative assignment assesses the learnt knowledge and application of methods and techniques. It consists of a coding exercise with accompanying report.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment

Via computer classes