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Unit of study_

BSTA5018: Machine Learning for Biostatistics (MLB)

Semester 2, 2023 [Online] - Camperdown/Darlington, Sydney

Recent years have brought a rapid growth in the amount and complexity of health data captured. Data collected in imaging, genomics, health registries, wearables, and among other applications call for new statistical techniques in both predictive and descriptive learning. Machine learning algorithms for classification and prediction complement classical statistical tools in the analysis of these data. This unit will cover modern machine learning methods particularly useful for large and complex health data. Topics include: linear regression and K-nearest neighbours; classification; bootstrapping and cross-validation resampling methods; model selection and regularization; non-linear approaches including splines and generalised additive models; and tree-based methods. The statistical software R will be used throughout the unit.

Unit details and rules

Unit code BSTA5018
Academic unit Public Health
Credit points 6
Prohibitions
? 
None
Prerequisites
? 
(PUBH5010 or BSTA5011 or CEPI5100) and (BSTA5007 or BSTA5210 or PUBH5217)
Corequisites
? 
None
Assumed knowledge
? 

None

Available to study abroad and exchange students

No

Teaching staff

Coordinator Andrew Grant, andrew.grant1@sydney.edu.au
Lecturer(s) Andrew Grant, andrew.grant1@sydney.edu.au
Armando Teixeira-Pinto, armando.teixeira-pinto@sydney.edu.au
Type Description Weight Due Length
Skills-based evaluation Practice exercise 1
Interpretation and implementation of several methods
10% -
Due date: 28 Aug 2023 at 23:59
3h
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Skills-based evaluation Practical exercise 2
Interpretation and implementation of several methods
10% -
Due date: 23 Oct 2023 at 23:59
3h
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Assignment 1
Interpretation and implementation of several methods
40% -
Due date: 11 Sep 2023 at 23:59
2 weeks
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Assignment Assignment 2
Interpretation and implementation of several methods
40% -
Due date: 06 Nov 2023 at 23:59
2000 words
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7

Assessment summary

Assessment will be by 2 main assignments, and 2 sets of practical exercises 

 

Assessment criteria

Grade

Mark Range

Description

AF

Absent fail

Range from 0 to 49

To be awarded to students who fail to demonstrate the learning outcomes for the unit at an acceptable standard through failure to submit or attend compulsory assessment tasks or to attend classes to the required level. 

FA

Fail

Range from 0 to less than 50

To be awarded to students who, in their performance in assessment tasks, fail to demonstrate the learning outcomes for the unit at an acceptable standard.

PS

Pass

Range from 50 to less than 65

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an acceptable standard

CR

Credit

Range from 65 to less than 75

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a good standard

D

Distinction

Range from 75 to less than 85

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at a very high standard

HD

High distinction

Range from 85 to 100 inclusive

To be awarded to students who, in their performance in assessment tasks, demonstrate the learning outcomes for the unit at an exceptional standard

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.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

If no extension has been given, 5% of the earned mark for an assignment will be deducted for each day that an assignment is late, up to a maximum of 50%. NOTE: It is not the intention of this late penalty policy to cause a student to fail the unit when otherwise they would have passed. If deductions for late assignments result in the final unit mark for a student being less than 50, when otherwise it would have been 50 or greater, the student's final mark will be exactly 50.

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 Module 1 - Introduction to Machine Learning Independent study (10 hr) LO1
Week 02 Module 2 - Regression and Classification Independent study (20 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 04 Module 3 - Resampling methods Independent study (10 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 05 Module 4 - Regularisation and model selection Independent study (20 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 07 Module 5 - Beyond linearity Independent study (20 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 09 Module 6 - Beyond additivity Independent study (30 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 12 Module 7 - Unsupervised learning Independent study (10 hr) LO3 LO4 LO5 LO6 LO7
Week 13 Module 8 - Elective topic (Neural networks, ensemble learning, Adaboost) Independent study (10 hr) LO1 LO5 LO6 LO7

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. Recognise situations where machine learning methods can offer advantages over traditional statistical modelling approaches to data analyses in health applications
  • LO2. Recognise and explain the differences between the goals of description and prediction
  • LO3. Determine and implement appropriate machine learning approaches for description and prediction in real-world health applications
  • LO4. Measure and explain the uncertainty of the results of analyses using machine learning approaches
  • LO5. Interpret the results of analyses using machine learning in light of the assumptions required, the quality of input data, and the sensitivity to the specific technique implemented
  • LO6. Critically appraise published papers concerning machine learning applications for classification or prediction in health
  • LO7. Effectively communicate results of analyses in language suitable for a clinical or epidemiological journal

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.

Disclaimer

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