Description
Aims:
This module provides a broad introduction to probabilistic modelling. The module starts with a review of probability theory and Bayesian reasoning, before moving to more advanced techniques for approximate Bayesian inference (variational inference, expectation propagation, sampling, to name a few) and finally covering Bayesian machine learning models such as Gaussian processes. These core principles and algorithms will be presented alongside example applications.Ìý
The aims are to:Ìý
- Provide an understanding of fundamentals of probabilistic models and their applications.ÌýÌý
- Capacitate Machine Learning and Artificial Intelligence practitioners in the development and deployment of uncertainty quantification and management in machine learning pipelines.ÌýÌý
- Capacitate those individuals to be effective team players in interdisciplinary research groups/ organisations and institutions that utilise such modelling approaches.
Intended learning outcomes:
On successful completion of the module, a student will be able to:Ìý
- Demonstrate understanding of the fundamental basic principles of probability theory and Bayesian inference.Ìý
- Implement and use key concepts, issues, and practices when training and modelling with probabilistic models.Ìý
- Solve data challenges spanning different application domains and core learning tasks with Bayesian machine learning algorithms.Ìý
- Demonstrate their acquired skills by attacking several real-world challenges using the techniques learned.Ìý
Indicative content:
The following are indicative of the topics the module will typically cover:
- Introduce to the fundamentals of probability theory and Bayesian modelling learning alongside their applications in the real-world.
- Probabilistic Modeling (e.g., graphical models, latent variable models, hidden Markov models.)Ìý
- Frequentist Inference.Ìý
- Bayesian Inference (e.g., variational inference, expectation propagation, sampling.)Ìý
- Bayesian Machine Learning (e.g., Bayesian linear regression, Gaussian processes.)Ìý
Requisites:
To be eligible to select this module as an optional or elective, a student must be registered on a programme and year of study for which it is formally available.
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 19th August 2024.
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