• Class Number 2541
  • Term Code 3430
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Anton Westveld
    • Dr Anton Westveld
  • 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 classification techniques on qualitative response variables.
  4. Assess models based on resampling methods.
  5. Carry out model selection based on regularisation methods.
  6. Utilise tree-based methods for regression and classification problems.
  7. Perform basic unsupervised learning techniques, such as clustering analysis and principal component analysis.

Research-Led Teaching

An important component of this course is a final project, which will allow students to think creatively about potential solutions to data analytic problems.

Additional Course Costs

Students should have access to a computer which is able to operate the current versions of R and RStudio.

Examination Material or equipment

There are no exams in the course. However there is a final project which is due during the exam period.

Required Resources

Required Texts:

  • James, Witten, Hastie,, and Tibshirani. 2021. An Introduction to Statistical Learning (second edition). Springer.
  • The authors provide a free e-book for downloading at https://statlearning.com/

  • Hastie, Tibshirani, and Friedman. 2009. The Elements of Statistical Learning: Data Mining, Inference, and Prediction (second edition). Springer.
  • The authors provide a free e-book for downloading at https://web.stanford.edu/~hastie/ElemStatLearn//

Staff Feedback

Students will be given feedback in the following forms in this course:

  • Self-study feedback in tutorials
  • Self-study feedback from assignments
  • Group in class or written feedback on performance in assignments
  • Individual feedback on student performance in assessment tasks via Turnitin

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.

Other Information

Support for Students

The University offers a number of support services for students. Information on these is available online from http://students.anu.edu.au/studentlife/

Communication via Email

If I, or anyone in the School, College or University administration, need to contact you, we will do so via your official ANU student email address, which you need to check regularly. If you have any questions for the teaching and course convenor make sure you email them using your ANU email address. Emails from personal email accounts will not be answered.


Students are expected to check the Wattle site for announcements about this course, e.g. changes to timetables or notifications of cancellations.

Assessment Requirements

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.


Your final mark for the course will be based on the raw marks allocated for each of your assessment items. However, your final mark may not be the same number as produced by that formula, as marks may be scaled. Any scaling applied will preserve the rank order of raw marks and may be either up or down.


This course is not co-taught.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to Statistical Learning
2 Regression - Review Assignment 1 is Released; Tutorials Begin
3 Classification
4 Resampling Methods Assignment 1 is Due
5 Multiple Testing
6 Linear Model Selection and Regularisation Feedback for Assignment 1
7 Moving Beyond Linearity Assignment 2 is Released
8 Tree-Based Methods
9 Support Vector Machines Assignment 2 is Due
10 Unsupervised Learning Final Project is Released
11 Time Permitting: Statistical Causality (or another topic) Feedback for Assignment 2
12 Time Permitting: Deep Learning (or another topic)

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 % 15/03/2024 29/03/2024 1,2
Assignment 2 25 % 03/05/2024 17/05/2024 1,2,3,4,5
Final Project - Data Analysis and Competition 60 % 03/06/2024 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.

As a further academic integrity control, students may be selected for a 15 minute individual oral examination of their written assessment submissions. 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.

Assessment Task 1

Value: 15 %
Due Date: 15/03/2024
Return of Assessment: 29/03/2024
Learning Outcomes: 1,2

Assignment 1

The compulsory assignment will require the use of R (and RStudio) to analyse real data and then to summarise and report the findings of the analysis. The assignment is designed to focus on materials from Weeks 1 to 3, including material from the requisite regression course. Students are expected to complete the assignment individually and submit it via Turnitin. The assignment will be released at least two weeks before it is due.

Assessment Task 2

Value: 25 %
Due Date: 03/05/2024
Return of Assessment: 17/05/2024
Learning Outcomes: 1,2,3,4,5

Assignment 2

The compulsory assignment will require the use of R (and RStudio) to analyse real data and then to summarise and report the findings of the analysis. This assignment is designed to focus on materials from Weeks 4 to 7. Students are expected to complete the assignment individually and submit it via Turnitin. The assignment will be released at least two weeks before it is due.

Assessment Task 3

Value: 60 %
Due Date: 03/06/2024
Return of Assessment: 27/06/2024
Learning Outcomes: 1,2,3,4,5,6,7

Final Project - Data Analysis and Competition

The compulsory final project is designed to apply many of the statistical learning ideas you have been introduced throughout the course and requires the use of R (and RStudio) to analyse real data. In addition, students will engage in a prediction competition, based on a withheld test data set. Students are required to communicate their findings in a formal written report. The format of the report will be outlined when the project is released. Students are expected to complete this project individually. The project report will be submitted via Turnitin and the prediction competition will be held through Kaggle.

Note that the project will be released in Week 10 (release date 2024-05-10) and will be due at the beginning of the second week of the examination period (due date 2024-06-03).

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

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

Late submission of the two assignments is allowed. However, the penalty is 5% per working day. Also, assignments will not be accepted after the earlier of the following: (1) The tenth working day after the due date; or, (2) The date specified in the class summary for the return of the assessment item.

Submission of the final project follows rules governing final exams.

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 Anton Westveld
+612 6125 5122

Research Interests

Research interests include Bayesian methodology and theory and statistical methods for interaction/relational data.

Dr Anton Westveld

Monday 10:00 11:00
Monday 10:00 11:00
Dr Anton Westveld
+612 6125 5122

Research Interests

Dr Anton Westveld

Monday 10:00 11:00
Monday 10:00 11:00

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