PhD Supervision.

Research Projects:

• An Analysis of Different Machine Learning and Inference Methodologies on Edge Devices
• Temporal Graph Representation and Comparison

MSc Supervision.

Machine Learning Projects:

• AI and Dermatology
• Detecting Clouds in Satellite Images
• Segmenting Marine Life Images

Dissertation Supervision.

Machine Learning Projects:

• Image Colourisation
• Deep Learning for Image Inpainting
• Diagnosing the Parkinson’s Disease

Lectures, Practicals, Coursework.

What is Deep Learning? Applications of Deep Learning, Loss functions and Backpropagation, training and optimisation,
Classification/Regression, Convolutional Neural Networks, Computer Vision applications, Recurrent Neural Networks,
Natural Language Processing applications, Improved training and performance of neural networks, Generative models,
Ethics and challenges of Deep Learning, Deep Reinforcement Learning, embeddings and Representation Learning.

Practicals.

Software development practices, requirements discovery and analysis, software Design, testing, human computer interaction, software quality and process, project management, etc.

Practicals.

Artificial Intelligence Search: heuristic searches, local searches, simulated annealing, genetic algorithms, etc.
Graphics: 3D graphics construction, rendering, modelling, transformation, shading, texture mapping, OpenGL, etc.
Digital Communication: entropy, information theory, lossless compression, encryption algorithms, etc.
Image Processing: transformation functions, histograms, spatial linear and nonlinear filters, JPEG, colour spaces, etc.

Practicals.

Systems programming: C, Bash, etc.
Functional programming, Haskell.
Object oriented programming: Java, Python.
Web Programming: HTML, JavaScript, PHP.

Practicals.

Real-Time Systems, C.

Lectures and Practicals.

C, C++, Java.

Lectures.

Concepts and principles, design, automation systems and enterprise planning.

Lectures and Coursework.

Introduction, architecture, lexical and syntax analysis, parsing methods, error recovery, semantic analysis, run-time environments, etc.

Lectures and Coursework.

Stochastic search methods, combinatorial optimisation, soft-computing methods, principles of machine learning, neural networks, image processing and computer vision, etc.

Lectures.

Graduate level course for computer science students.