Module overview
Linked modules
Pre-requisite: COMP3223 or COMP6245
Aims and Objectives
Learning Outcomes
Subject Specific Intellectual and Research Skills
Having successfully completed this module you will be able to:
- Critically appraise the merits and shortcomings of model architectures on specific problems.
- Critical appraisal of recent scientific literature in Bayesian inference & active learning.
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 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 |
|---|---|
| Completion of assessment task | 60 |
| Lecture | 24 |
| Specialist Laboratory | 20 |
| Wider reading or practice | 46 |
| 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