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Unit of study_
PHYS5160: Bayesian Inference and Machine Learning (HDR)
2024 unit information
The need to make sense of confusing, incomplete and noisy data is a problem central to virtually all branches of science. The underlying requirement is to draw robust, unbiased and insightful inferences from the data. After taking this course you should have a working knowledge of common data inference and model-fitting methods, and of machine learning techniques. You should be able to implement the model-fitting algorithms discussed here in your own code and use it to determine parameters from incomplete or noisy data. You will have a conceptual understanding of modern machine-learning techniques, including basic neural networks, and be able to implement your own network to solve a problem. Moreover, you will have the prerequisite knowledge to implement more complex machine learning architectures such as deep learning, using the wide range of available tools. The course is aimed to equip anyone faced with quantitative data, from any field of science, technology or social science, with practical tools to be deployed in arriving at concrete conclusions that are directly applicable to their work. The emphasis is therefore on usable tools for the practitioner, rather than a theoretical understanding.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Postgraduate |
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| Academic unit | Physics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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None |
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Corequisites:
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None |
| Prohibitions:
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PHYS4016 |
| Assumed knowledge:
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Undergraduate level introductory statistics. Prior exposure to coding and matrices is beneficial |
At the completion of this unit, you should be able to:
- LO1. Understand the guiding philosophy of the Bayesian approach to probability and its application to data processing and parameter estimation, contrasting this to a frequentist approach.
- LO2. Apply Bayesian principles in data analysis, specifically in the roles of hypothesis testing and parameter estimation.
- LO3. Have a fundamental understanding of the very distinct ways in which Bayesian statistics differs from much of 20th century practice (Frequentist Statistics), and in particular in the domain of how hypothesis testing is framed and conducted.
- LO4. Understand the meaning and application of Maximum-Entropy techniques in determining maximally ignorant probability distributions, and the extraction of information through image deconvolution.
- LO5. The ability to apply these concepts to develop statistical and machine learning models, and to solve qualitative and quantitative problems in scientific and engineering contexts, using appropriate mathematical and computing techniques as necessary.
- LO6. Understand the challenge posed by incomplete and noisy data, and the importance of using robust tools to infer underlying information and reliably quantify uncertainty in inferences or predictions.
- LO7. Be able to apply inference and model-fitting tools (such as Markov chain Monte Carlo) to train models on real data, and have a fundamental understanding and intuition for how these tools work, and their strengths and weaknesses.
- LO8. Understand the utility of unsupervised machine learning and how it contrasts with traditional approaches to categorisation and inference, and be able to recognise its dangers and limitations.
- LO9. Understand the fundamental principles of neural networks and deep learning: be able to recognise the types of problems that are suited to such techniques, and appreciate their power as compared to previous approaches.
- LO10. Be able to apply neural network based machine-learning techniques to actual data sets to perform useful data inference, and be prepared for implementing more complex machine learning models using the wide variety of available frameworks.
Unit availability
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