Module overview
Linked modules
Pre-requisite: COMP3223 or COMP6245
Aims and Objectives
Learning Outcomes
Knowledge and Understanding
Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:
- The key factors that have made Bayesian inference & active learning g successful for various applications.
- Underlying mathematical and algorithmic principles of Bayesian inference & active learning.
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.
Subject Specific Practical Skills
Having successfully completed this module you will be able to:
- 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.
- Apply existing Bayesian and active learning methods to real applications.
Syllabus
Reasoning under uncertainty
- Bayesian paradigm
- Recognising sources of uncertainty and approaches to handling them
- Maximum a posteriori estimation
Approximate inference
- Local linearisation
- Markov Chain Monte Carlo
- Sequential Monte Carlo
- Variational inference
Bayesian deep learning
- Variational autoencoders
- Weight uncertainty & Bayes by backprop
Temporal-difference learning
Active and reinforcement learning
- Uncertainty, regret and reward
- Markov decision processes
- Decision optimisation
In-depth case study (one or more taken from the following applications):
- Robot Localisation & Motion planning
- Machine listening
Spatio-temporal modelling
Learning and Teaching
Teaching and learning methods
Lectures and labs
Type | Hours |
---|---|
Wider reading or practice | 46 |
Lecture | 24 |
Completion of assessment task | 60 |
Specialist Laboratory | 20 |
Total study time | 150 |
Resources & Reading list
Textbooks
David MacKay (2012). Information Theory, Inference, and Learning Algorithms. Cambridge University Press.
Kevin Murphy (2021). Probabilistic Machine Learning . MIT Press.
David Barber (2010). Bayesian Reasoning and Machine Learning. Cambridge University Press.
Assessment
Summative
This is how we’ll formally assess what you have learned in this module.
Method | Percentage contribution |
---|---|
Continuous Assessment | 100% |
Referral
This is how we’ll assess you if you don’t meet the criteria to pass this module.
Method | Percentage contribution |
---|---|
Set Task | 100% |
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.
Method | Percentage contribution |
---|---|
Set Task | 100% |
Repeat Information
Repeat type: Internal & External