• Class Number 2555
  • Term Code 3430
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Tao Zou
    • Dr Tao Zou
  • Class Dates
  • Class Start Date 19/02/2024
  • Class End Date 24/05/2024
  • Census Date 05/04/2024
  • Last Date to Enrol 26/02/2024
SELT Survey Results

Statistical Learning is a course designed for students who need to carry out statistical analysis, or “learning”, from real data. Emphasis will be placed on the development of statistical concepts and statistical computing. The content will be motivated by problem-solving in many diverse areas of application. This course will cover a range of topics in statistical learning including linear and non-linear regression, classification techniques, resampling methods (e.g., the bootstrap), regularisation methods, tree based methods and unsupervised learning techniques (e.g. principle components analysis and clustering). 

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Use packages and process output relating to statistical learning in the statistical computing package R. 
  2. Fit linear and non-linear regression models and analyse relationships between a response variable and covariates. 
  3. Perform in-depth classification techniques on qualitative response variables. 
  4. Assess in detail models based on resampling methods. 
  5. Carry out model selection based on a variety of regularisation methods. 
  6. Utilise tree-based methods. 
  7. Perform unsupervised learning techniques. 

Research-Led Teaching

Where possible, topics will be related to current research problems and reflect real world situations to emphasize the use of the techniques covered.


Additional Course Costs

The only other additional course costs are a calculator, textbook (if purchased) and printing materials.

Examination Material or equipment

There is no examination in this course. Please see Assessment sections for details and required material.


Required Resources

Class materials, including detailed lecture notes, slides, lecture demonstrations, tutorials, assignments and other relevant materials, will be made available on the class web page on Wattle. It is essential that you visit the class Wattle site regularly.

Recommended Text

James, G., Witten, D., Hastie, T., & Tibshirani, R. An introduction to statistical learning. First Edition or Second Edition. Springer.

A free ebook copy of the textbook is available at: https://statlearning.com/

Staff Feedback

Students will be given feedback (through both verbal and written comments) in the following forms in this course:

• To the whole class during lectures.

• Within tutorials.

• Individually during consultation hours.

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to statistical learning and getting to know R.
2 Review of linear regression. Lectures and tutorials.
3 Classification. Lectures and tutorials. Assignment 1 open
4 Classification. Lectures and tutorials.
5 Resampling methods. Lectures and tutorials. Assignment 1 due
6 Linear model selection and regularisation I. Lectures and tutorials.
7 Introduction to unsupervised learning I. Linear model selection and regularisation II. Lectures and tutorials.
8 Moving beyond linearity. Lectures and tutorials.
9 Moving beyond linearity. Lectures and tutorials. Assignment 2 open
10 Tree-based methods. Lectures and tutorials. Final project open
11 Introduction to unsupervised learning II. Lectures and tutorials. Assignment 2 due
12 Various topics of interest (e.g., generalised additive models, support vector machines, etc). Lectures and tutorials.

Tutorial Registration

Tutorial registration will be available two weeks prior to the beginning of the semester and will close at the end of week 1. More details can be found on the Timetable webpage. https://www.anu.edu.au/students/program-administration/timetabling.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 15 % 20/03/2024 28/03/2024 1,2,3
Assignment 2 25 % 15/05/2024 24/05/2024 1,2,3,4,5
Final Project 60 % 05/06/2024 27/06/2024 1,2,3,4,5,6,7

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.


Course content delivery will take the form of weekly on-campus lectures (recorded and available via echo360 on Wattle) and weekly tutorials, all delivered on campus. Weekly consultations with the lecturer and the tutor(s) will be conducted over Zoom.


There is no examination in this course.

Assessment Task 1

Value: 15 %
Due Date: 20/03/2024
Return of Assessment: 28/03/2024
Learning Outcomes: 1,2,3

Assignment 1

Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials of Weeks 1-4. Assignments may include derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in class and on Wattle. Assignments are expected to be in a PDF or Word file.

Estimated return date: The week after submission.

Assessment Task 2

Value: 25 %
Due Date: 15/05/2024
Return of Assessment: 24/05/2024
Learning Outcomes: 1,2,3,4,5

Assignment 2

Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials of Weeks 1-10. Assignments may include derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in class and on Wattle. Assignments are expected to be in a PDF or Word file.

Estimated return date: The week after submission.

Assessment Task 3

Value: 60 %
Due Date: 05/06/2024
Return of Assessment: 27/06/2024
Learning Outcomes: 1,2,3,4,5,6,7

Final Project

Turnitin submission. The students are expected to complete this project individually. This final project will be based on all the materials covered throughout the duration of the semester. Students will be provided with further details regarding the final project in Week 10. This project requires the use of R to analyse real data. This project is designed to apply all the materials introduced in this course to analyse real datasets assigned by the course convener, as well as to predict some on-hold data. Written reports for this project (10 pages maximum for the main manuscript and 20 pages maximum for the appendix based on the format below, and all the R code should be relegated to the appendix) are expected to be submitted via Turnitin. Turnitin similarity check will be conducted for all the submitted reports.

Report Format – PDF or Word Upload

Use Australian English spelling. All pages (uploaded in PDF or Word form) must be as follows:

• Black type, or occasional coloured type for highlighting purposes;

• Single column;

• White A4 size paper with at least 0.5 cm margin on each side, top and bottom;

• Text must be size 12 point Times New Roman or an equivalent size before converting to PDF format and must be legible to assessors;

• References and appendices only can be in 10 point Times New Roman or equivalent.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

Any student identified, either during the current semester or in retrospect, as having used ghost writing services will be investigated under the University’s Academic Misconduct Rule. You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

There is no hardcopy submission in the course.

Late Submission

No submission of assessment tasks without an extension after the due date will be permitted. If an assessment task is not submitted by the due date, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Returning Assignments

The marked assignments will be returned online.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Resubmission of Assignments

It will not be possible for assignments to be resubmitted.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Tao Zou
6125 6221

Research Interests

Covariance regression modelling, network data modelling, financial statistics, environmental statistics, dependent data analysis and big data analysis

Dr Tao Zou

Wednesday 14:00 15:00
Wednesday 14:00 15:00
Dr Tao Zou
6125 6221

Research Interests

Dr Tao Zou

Wednesday 14:00 15:00
Wednesday 14:00 15:00

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions