Machine Learning (I)

Learning Outcomes

Knowledge and Understanding

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

• Understand the relationship between machine learning and biological learning
• Demonstrate knowledge of simple unsupervised learning methods
• Demonstrate knowledge of simple supervised learning methods

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Characterise data in terms of explanatory models
• Use data to reinforce one/few among many competing explanatory hypotheses
• Be able to derive simple learning algorithms for regression and classification
• Explore the real-world impact of training complex AI models

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Gain facility in designing experiments and measuring performance with appropriate metrics
• Systematically work with data to learn new patterns or concepts

Historical Perspective

• Biological motivations (e.g. the McCulloch and Pitts neuron, Rosenblatt's perceptron, Hebbian learning).
• Statistical motivations (Bayes rule, etc.)

Theory

• Generalisation: What is learning from data?
• The power of machine learning methods: What is a learning algorithm? What can they do?
• Experiment design & measuring performance

Supervised Learning

• Classification using Bayesian principles
• Fisher's Linear Discriminant
• Linear regression & simple regularisation
• Learning linear decision boundaries
• Introduction to neural networks & multi-layer perceptrons (MLPs)
• Features and discriminant analysis

Optimisation

• Basics of gradient-based optimisation (simple Gradient Descent and SGD)

Data handling and unsupervised learning

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

Teaching and learning methods

The module consists of:

• Lectures
• Combined tutorials and computing laboratory sessions

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.