Outline

Overview

The objectives of this unit of study are to develop an understanding of modern computationally intensive methods for statistical learning, inference, exploratory data analysis and data mining. Advanced computational methods for statistical learning will be introduced, including clustering, density estimation, smoothing, predictive models, model selection, combinatorial optimisation methods, sampling methods, the Bootstrap and Monte Carlo approach. In addition, the unit will demonstrate how to apply the above techniques effectively for use on large data sets in practice.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	STAT5002 or equivalent introductory statistics course with a statistical computing component
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Jaslene Huan Lin, huan.lin@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Final Exam Final written exam	50%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
In-class quiz	Tutorial Quizzes In-class quizzes with a mix of multiple-choice and short-answer questions.	15%	Multiple weeks	30 minutes	AI allowed
Outcomes assessed:
Written work	Project: Plan and EDA Group project plan and exploratory data analysis	8%	Week 07	6 pages	AI allowed
Outcomes assessed:
Data analysis	Project: Report Written group project report	15%	Week 12	10 pages	AI allowed
Outcomes assessed:
Presentation	Project: Presentation Oral group presentation delivered during your tutorial in week 12	10%	Week 12	10 minutes including Q&A	AI allowed
Outcomes assessed:
Contribution	Project: Peer Review Provide individual feedback on presentations	2%	Week 12	3 hours	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Tutorial Quizzes: The best 3 out of 4 quizzes will contribute to your final grade. No simple extension will be granted, and you won’t need to apply for special consideration if you miss one quiz.
Group project: The group presentation has four components:
- Project Plan and EDA: Each group will submit one report due on Sunday at 23:59 pm in Week 7.
- Presentation: Group presentation delivered during Week 12’s lecture and tutoral time.
- Peer Review: This is an individual task. You will be expected to provide constructive feedback for other presentations that you view in your tutorial.
- Report: Your group will compose a final report. This is done as a group, i.e. one submission per group.
Final Exam: Supervised exam in the formal exam period.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction of a very high standard, a credit of a good standard, and a pass of an acceptable standard.

Result name	Mark range	Description
High Distinction	85 - 100	Representing complete or close to complete mastery of the material.
Distinction	75 - 84	Representing excellence, but substantially less than complete mastery.
Credit	65 - 74	Representing a creditable performance that goes beyond routine knowledge and understanding, but less than excellence.
Pass	50 - 64	Representing at least routine knowledge and understanding over a spectrum of topics and important ideas and concepts in the course.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

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 University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

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.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Basics of statistical computing and visualisation	Lecture and tutorial (3 hr)
Week 02	Regression and smoothing	Lecture and tutorial (3 hr)
Week 03	Density estimation	Lecture and tutorial (3 hr)
Week 04	High-dimensional visualisation and analytics in R	Lecture and tutorial (3 hr)
Week 05	Classification with R	Lecture and tutorial (3 hr)
Week 06	Cross-validation and bootstrapping	Lecture and tutorial (3 hr)
Week 07	Treatment of missing values	Lecture and tutorial (3 hr)
Week 08	Feature and model selection	Lecture and tutorial (3 hr)
Week 09	Tree classifiers and ensembles	Lecture and tutorial (3 hr)
Week 10	Monte Carlo methods 1	Lecture and tutorial (3 hr)
Week 11	Monte Carlo methods 2	Lecture and tutorial (3 hr)
Week 12	Final presentation	Lecture and tutorial (3 hr)
Week 13	Revision	Lecture and tutorial (3 hr)

Attendance and class requirements

You are expected to actively participate in the weekly lectures and tutorials, and collaborate with your peers on the group project.

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.

Required readings

Primary text:

An Introduction to Statistical Learning (with Applications in R), Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani, Second edition, 2021, Springer.
- Available online: https://statlearning.com

Additional references:

Computational Statistics (Second Edition), Geof Givens, Jennifer Hoeting, 2013, Wiley.
Applied Predictive Modeling, Max Kuhn, Kjell Johnson, 2013, Springer.
Introductory Statistics with R, Peter Dalgaard, 2008, Springer.
R for Data Science, Wickham & Grolemund, 2017, O'Reilly.
- Available online (with 2nd edition draft): https://r4ds.had.co.nz/
Elements of Statistical Learning, Hasties, Tibsharani, Friedman, 2008, Springer (More advanced textbook)
- Available online: https://web.stanford.edu/~hastie/ElemStatLearn/

Learning outcomes

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.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Formulate domain/context specific questions and identify appropriate statistical analysis.
LO2. Formulate, evaluate and interpret appropriate statistical models to describe the relationships between multiple factors.
LO3. Perform statistical machine learning using a given classifier and create a cross-validation scheme to calculate the prediction accuracy.
LO4. Understand, perform and interpret various unsupervised machine learning methods
LO5. Construct and implement resampling techniques to understand the behaviour of statistical models.
LO6. Create a reproducible report to communicate outcomes using a programming language.

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
Learning outcomes

Responding to student feedback

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

To enhance exam preparedness, we are replacing the weekly online assignments with in-class tutorial quizzes. These quizzes are designed to help students better prepare for the invigilated final exam.

Additional information

Mathematics and Statistics student portal

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.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

STAT5003: Computational Statistical Methods

Semester 2, 2025 [Normal evening] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links