Core Module Information
Module title: Advanced Machine Learning

SCQF level: 10:
SCQF credit value: 20.00
ECTS credit value: 10

Module code: SET10118
Module leader: Valerio Giuffrida
School School of Computing
Subject area group: Software Engineering
Prerequisites

N/A

Description of module content:

This module aims to extend the Fundaments of machine learning module. In particular, students will gain a deeper knowledge of modern machine learning algorithms, with an emphasis of neural networks for image analysis tasks (e.g., classification, regression, segmentation). The syllabus of this module includes:

- Probability theory: introduction, discrete and continuous probability function, Bayesian classifier, information theory.
- Image analysis: colour spaces,convolution, edge detection, image representation, image descriptors
- Neural networks: multi-layer perceptron, convolutional layers, initialisation, optimisers

In the last part of the module, the students will learn about state-of-the-art deep neural network architectures, putting them in the context of classification and regression tasks.

Learning Outcomes for module:

LO1: Critically evaluate the use of machine learning algorithms
LO2: Develop new skills in image analysis using deep networks
LO3: Critically evaluate the different layers in a deep network
LO4: Critically evaluate the different activation functions and optimisers
LO5: Design, develop, and evaluate customised deep networks applications in Python
LO6: Critically report the results in a scientific form and draw appropriate conclusions.

Full Details of Teaching and Assessment

Indicative References and Reading List - URL:
Contact your module leader