Foundations of Machine Learning (MSc)

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Gain a critical appreciation of the latest research issues
• Derive learning algorithms from first principles and quantifying their performance
• Characterise data in terms of explanatory models

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 using machine learning make predictions in a scientific computing environment

Knowledge and Understanding

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

• The mathematical foundations of machine learning as inspired by biological learning
• Underlying mathematical principles from probability, linear algebra and optimisation

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.