Bayesian, Active & Reinforcement Learning

Learning Outcomes

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

• Critical appraisal of recent scientific literature in Bayesian inference & active learning.
• Critically appraise the merits and shortcomings of model architectures on specific problems.

Knowledge and Understanding

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

• Underlying mathematical and algorithmic principles of Bayesian inference & active learning.
• The key factors that have made Bayesian inference & active learning g successful for various applications.

Subject Specific Practical Skills

Having successfully completed this module you will be able to:

• Apply existing Bayesian and active learning methods to real applications.
• Gain facility in working with Bayesian paradigm and active learning methodology in order to create and evaluate their performance and applicability in different application domains.

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.