Machine Learning Technologies (MSc)

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Appreciation of the landscape of tools used for modern machine learning
• Characterise data in terms of explanatory models
• Critically appraise the uses of Deep Learning

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

• The algorithmic basis of machine learning algorithms
• Implementation issues in learning algorithms and the evaluation of their performance.

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Systematically work with data to learn new patterns or concepts
• Gain expertise in working with machine learning to make predictions in a scientific computing environment

Historical Perspective

• Biological motivations: the McCulloch and Pitts neuron, Hebbian learning.
• Conceptual motivations

Tools in machine learning

• Libraries
• Implementing and evaluating algorithms

Supervised Learning

• Perceptron Algorithm
• Support Vector Classification and Regression
• Neural networks/multi-layer perceptrons (MLP)

Data handling and unsupervised learning

• Principal Components Analysis (PCA)
• K-Means clustering

Regression and Model-fitting Techniques

• Linear regression

Deep Learning

• Deep Neural Networks (CNN, RNN)

Case Studies

• Example applications: Speech, Vision, Natural Language, Bioinformatics.

Lectures, labs and guided self-study

Summative

This is how we’ll formally assess what you have learned in this module.

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.