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
Academic unit | Physics Academic Operations |
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Credit points | 6 |
Prerequisites
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144 credit points of units of study including (12cp of MATH1001 or MATH1002 or MATH1003 or MATH1004 or MATH1005 or MATH1021 or MATH1023 or MATH1064 or MATH1115 or MATH19XX or DATA1X01) |
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 |
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 | |
Tutor(s) | Alison Wong, a.wong@sydney.edu.au |