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
Unit code | PHYS5160 |
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Academic unit | Physics Academic Operations |
Credit points | 6 |
Prohibitions
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PHYS4016 |
Prerequisites
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None |
Corequisites
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None |
Assumed knowledge
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Undergraduate level introductory statistics. Prior exposure to coding and matrices is beneficial |
Available to study abroad and exchange students | Yes |
Teaching staff
Coordinator | Peter Tuthill, peter.tuthill@sydney.edu.au |
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Lecturer(s) | Barnaby Norris, barnaby.norris@sydney.edu.au |
Peter Tuthill, peter.tuthill@sydney.edu.au |