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Unit of study_

PHYS5160: Bayesian Inference and Machine Learning (HDR)

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

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
Academic unit Physics Academic Operations
Credit points 6
Assumed knowledge

Undergraduate level introductory statistics. Prior exposure to coding and matrices is beneficial

Available to study abroad and exchange students


Teaching staff

Coordinator Peter Tuthill,
Lecturer(s) Barnaby Norris,
Peter Tuthill,
Type Description Weight Due Length
Supervised exam
PHYS5160 exam: Bayesian Data Inference and Machine Learning
Written problems in Bayesian Statistics, MCMC and Machine Learning.
40% Formal exam period 1.5 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Assignment Assignment 1
2-3 solved problems with working and codes submitted.
12% Week 05
Due date: 01 Sep 2023 at 23:59
3-4 pages
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Assignment Assignment 2
2-3 solved problems with working and codes submitted.
12% Week 09
Due date: 06 Oct 2023 at 23:59
3-4 pages
Outcomes assessed: LO4 LO5 LO6 LO7
Assignment Assignment 3
2-3 solved problems with working and codes submitted.
12% Week 13
Due date: 03 Nov 2023 at 23:59
4-5 pages
Outcomes assessed: LO5 LO8 LO9 LO10
Tutorial quiz Tutorial Completion Quiz
Short submitted tutorial codes and solutions.
12% Weekly 30 minutes
Outcomes assessed: LO1 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2
Online task Lecture Quizzes
Brief quiz on weekly lectures, online, multiple-choice.
12% Weekly 15 minutes
Outcomes assessed: LO1 LO10 LO9 LO8 LO7 LO6 LO5 LO4 LO3 LO2

Assessment summary

Lecture Quiz: Content from each weeks lectures will be tested in the form of a short online quiz that probes the fundamental concepts for each week. This quiz should be completed, at the latest, prior to the Tutorial session each week. Total weight is 12% for all Lecture Quiz’s.

Tutorial Completion: Completion or substantial attempt at each week’s tutorial problems is rewarded with a total of 12% accumulated marks for the class. 

Assignments: Three assignments, each contributing 12%, will consist of worked problems requiring mathematical and coding knowledge taken from the content taught in this class.

Final Exam: Students will sit a 90-minute final exam consisting of worked problems testing concepts in mathematical and coding algorithms central to this class.  

The final exam is compulsory. Failure to submit will result in an absent fail grade (AF) for the unit.

If a second replacement exam is required, this exam may be delivered via an alternative assessment method, such as a viva voce (oral exam). The alternative assessment will meet the same learning outcomes as the original exam. The format of the alternative assessment will be determined by the unit coordinator

Detailed information for each assessment can be found on Canvas

Assessment criteria


High distinction

85 - 100

Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



75 - 84

Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



65 - 74

Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



50 - 64

Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.



0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard.


Absent fail

0 - 49

When you haven’t completed all assessment tasks or met the attendance requirements.

For more information see guide to grades.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

  • Deduction of 5% of the maximum mark for each calendar day after the due date.
  • After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

WK Topic Learning activity Learning outcomes
Week 01 Introduction to Bayesian Reasoning Independent study (1 hr) LO1
Bayesian Statistics: Fundamentals Independent study (1 hr) LO1
Bayesian Fundamentals Tutorial (2 hr) LO1
Week 02 Parameter Estimation Independent study (1 hr) LO1 LO2
Hypothesis Testing Independent study (1 hr) LO1 LO2
Bayesian Computations Tutorial (2 hr) LO1 LO2
Week 03 Model Comparison: contrasting Bayes and Frequentist methods Independent study (1 hr) LO1 LO2 LO3
Linear and Logistic Regression Independent study (1 hr) LO2
Bayesian Testing Tutorial (2 hr) LO2 LO3
Week 04 Introduction to MCMC Independent study (1 hr) LO5 LO6 LO7
MCMC: Tempering and Sampling Independent study (1 hr) LO5 LO6 LO7
Building MCMC codes Tutorial (2 hr) LO5 LO6 LO7
Week 05 Advanced MCMC: Affine Invariant Samplers Independent study (1 hr) LO5 LO6 LO7
Advanced MCMC: Hamiltonian Monte Carlo Independent study (1 hr) LO5 LO6 LO7
Advanced MCMC Tutorial (2 hr) LO5 LO6 LO7
Week 06 Nested Sampling Independent study (1 hr) LO5 LO6 LO7
Genetic Algorithms Independent study (1 hr) LO5 LO6 LO7
Advanced numerical methods Tutorial (2 hr) LO5 LO6 LO7
Week 07 Resampling and Approximate Bayesian Computation Independent study (1 hr) LO5 LO6 LO7
Introduction: Information and Entropy Independent study (1 hr) LO4 LO5 LO6
Resampling, Information, Entropy Tutorial (2 hr) LO4 LO5 LO6 LO7
Week 08 Information Criteria and Hierarchical Independent study (1 hr) LO5 LO6 LO7
Inverse Problems and Image Recovery Independent study (1 hr) LO4 LO5 LO6
Advanced Bayes & inverse problems Tutorial (2 hr) LO4 LO5 LO6 LO7
Week 09 Compressed Sensing Independent study (1 hr) LO4 LO5 LO6
Gaussian Processes Independent study (1 hr) LO5 LO6 LO7 LO8
Data dimensionality and Gaussian Processes Tutorial (2 hr) LO5 LO6 LO7 LO8
Week 10 Introduction to machine learning Independent study (1 hr) LO5 LO8 LO9
Dimensionality reduction and basic supervised learning Independent study (1 hr) LO5 LO8 LO9
Introduction to Machine Learning Tutorial (2 hr) LO5 LO7 LO8
Week 11 Introduction to neural networks Independent study (1 hr) LO5 LO8 LO9
Deep learning with neural networks Independent study (1 hr) LO5 LO8 LO9 LO10
Introduction to Neural Networks Tutorial (2 hr) LO5 LO8 LO9 LO10
Week 12 Deep learning architectures and applications for inference Independent study (1 hr) LO5 LO8 LO9 LO10
Generative and Bayesian neural networks Independent study (1 hr) LO8 LO9 LO10
Deep Learning Tutorial (2 hr) LO8 LO9 LO10
Week 13 Self-supervised learning and transformers Independent study (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Summary and Revision Independent study (1 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10
Summary and revision Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7 LO8 LO9 LO10

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

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.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1 Depth of disciplinary expertise

Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.

GQ2 Critical thinking and problem solving

Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.

GQ3 Oral and written communication

Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.

GQ4 Information and digital literacy

Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.

GQ5 Inventiveness

Generating novel ideas and solutions.

GQ6 Cultural competence

Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.

GQ7 Interdisciplinary effectiveness

Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.

GQ8 Integrated professional, ethical, and personal identity

An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.

GQ9 Influence

Engaging others in a process, idea or vision.

Outcome map

Learning outcomes Graduate qualities

This section outlines changes made to this unit following staff and student reviews.

first draft created


The School of Physics recognises that biases, bullying and discrimination, including but not limited to those based on gender, race, sexual orientation, gender identity, religion and age, continue to impact parts of our community disproportionately. Consequently, the School is strongly committed to taking effective steps to make our environment supportive and inclusive and one that provides equity of access and opportunity for everyone.

The School has three Equity Officers as a point of contact for students who may have a query or concern about any issues relating to equity, access and diversity. If you feel you have been treated unfairly, discriminated against, bullied or disadvantaged in any way, you are encouraged to talk to one of the Equity Officers or any member of the Physics staff.

More information can be found at

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  • Closed-toe shoes are mandatory 
  • Follow safety instructions in your manual, posted in laboratories, and from staff.
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  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory 
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