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Unit outline_

PHYS4016: Bayesian Data Inference and Machine Learning

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 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
Credit points 6
Prerequisites
? 
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
? 
None
Prohibitions
? 
None
Assumed knowledge
? 

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
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
Type Description Weight Due Length
Supervised exam
? 
Phys 4016 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

HD

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.

DI

Distinction

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.

CR

Credit

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.

PS

Pass

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.

FA

Fail

0 - 49

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

AF

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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 Bayes 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

No changes have been made since this unit was last offered.

EQUITY, ACCESS AND DIVERSITY STATEMENT

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 https://sydney.edu.au/science/schools/school-of-physics/equity-access-diversity.html

Any student who feels they may need a special accommodation based on the impact of a disability should contact Disability
Services: https://sydney.edu.au/study/academic-support/disability-support.html who can help arrange support.

Work, health and safety

We are governed by the Work Health and Safety Act 2011, Work Health and Safety Regulation 2011 and Codes of Practice. Penalties for non-compliance have increased. Everyone has a responsibility for health and safety at work. The University’s Work Health and Safety policy explains the responsibilities and expectations of workers and others, and the procedures for managing WHS risks associated with University activities.

General Laboratory Safety Rules

  • No eating or drinking is allowed in any laboratory under any circumstances
  • Closed-toe shoes are mandatory 
  • Follow safety instructions in your manual, posted in laboratories, and from staff.
  • In case of fire, follow instructions posted outside the laboratory door 
  • First aid kits, eye wash and fire extinguishers are located in or immediately outside each laboratory 
  • As a precautionary measure, it is recommended that you have a current tetanus immunisation. This can be obtained from University Health Service: unihealth.usyd.edu.au/

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.