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Unit of study_
PHYS4016: Bayesian Data Inference and Machine Learning
2025 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 physicists (and other scientists) with practical tools to be deployed in their work, rather than delivering more theoretical content.
Unit details and rules
Managing faculty or University school:
Science
| Study level | Undergraduate |
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| Academic unit | Physics Academic Operations |
| Credit points | 6 |
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Prerequisites:
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144 credit points of units of study including (12 credit points of MATH1001 or MATH1002 or MATH1003 or MATH1004 or MATH1005 or MATH1021 or MATH1023 or MATH1064 or MATH1X61 or MATH1X62 or MATH1971 or MATH1972 or MATH1115 or MATH19XX or DATA1X01) |
|---|---|
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Corequisites:
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None |
| Prohibitions:
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None |
| Assumed knowledge:
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48 credit points of 3000-level units of study and programming experience in Python |
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
This section lists the session, attendance modes and locations the unit is available in. There is a unit outline for each of the unit availabilities, which gives you information about the unit including assessment details and a schedule of weekly activities.
The outline is published 2 weeks before the first day of teaching. You can look at previous outlines for a guide to the details of a unit.
| Session | MoA ? | Location | Outline ? |
|---|---|---|---|
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Semester 2 2025
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Normal day | Camperdown/Darlington, Sydney |
Outline unavailable
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Modes of attendance (MoA)
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