Foundations of Machine Learning

Learning Outcomes

Knowledge and Understanding

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

• Underlying mathematical principles from probability, linear algebra and optimisation
• The relationship between machine learning and biological learning

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
• Gain a broad understanding of the latest research issues

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 facility in working with algorithms to handle data sets in a scientific computing environment

Historical Perspective - Biological motivations: the McCulloch and Pitts neuron, Hebbian learning. - Statistical motivations Theory - Generalisation: What is learning from data? - The power of machine learning methods: What is a learning algorithm? What can they do? Probability - Probability as representation of uncertainty in models and data - Bayes Theorem and its applications - Law of large numbers and the Multivariate Gaussian distribution Optimisation - Convexity - 1-D minimisation - Gradient methods in higher dimensions - Constrained optimisation Linear Algebra - Using matrices to find solutions of linear equations - Properties of matrices and vector spaces - Eigenvalues, eigenvectors and singular value decomposition Supervised Learning - Regression Analysis - Classification using Bayesian principles - Perceptron Learning - Support Vector Machines and introduction to Kernel methods - Neural networks/multi-layer perceptrons (MLP) - Features and discriminant analysis Data handling and unsupervised learning - Principal Components Analysis (PCA) - K-Means clustering - Spectral clustering Regression and Model-fitting Techniques - Linear regression - Polynomial Fitting - Kernel Based Networks 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.