Advanced Machine Learning

Learning Outcomes

Knowledge and Understanding

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

• Kernel methods for handling high dimensional and non-linear patterns
• Theoretical concepts and the motivations behind different learning frameworks
• Key concepts, tools and approaches for pattern recognition on complex data sets
• State-of-the-art algorithms such as Support Vector Machines and Bayesian networks

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Conceptually understand the role of pattern analysis and probabilistic modeling, together with the mathematical techniques this requires

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Be able to solve real-world machine learning tasks: from data to inference

Key concepts

• Supervised/Unsupervised Learning
• Loss functions and generalization
• Probability Theory
• Elements of Computational Learning Theory

Kernel Methods for non-linear data

• Support Vector Machines
• Kernels

Bayesian methods for using prior knowledge and data

• Bayesian inference
• Bayesian Belief Networks and Graphical models
• Gaussian Processes

Ensemble Learning

• Bagging
• Boosting
• Random Forest

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.