• Class Number 2828
  • Term Code 3230
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Anton Westveld
    • Dr Anton Westveld
  • Class Dates
  • Class Start Date 21/02/2022
  • Class End Date 27/05/2022
  • Census Date 31/03/2022
  • Last Date to Enrol 28/02/2022
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

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.

Required Resources

Required Texts:

  • 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). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

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.


The courses STAT3040, STAT4040, and STAT7040 are 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
11 Time Permitting: Deep Learning (or another topic) Final Project is Released
12 Time Permitting: Deep Learning (or another topic) Feedback for Assignment 2

Tutorial Registration

Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. information regarding enrollments for these options will be provided on Wattle no later than week one of the semester

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 10 % 18/03/2022 01/04/2022 1,2
Assignment 2 30 % 06/05/2022 29/05/2022 1,2,3,4,5
Final Project - Data Analysis and Competition 60 % 10/06/2022 30/06/2022 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 Misconduct 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 Integrity . 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.


The lectures will be delivered either on campus (recorded and available via echo360 on Wattle), live and recorded through Zoom, or on occasion as prerecorded videos. Consultations will be live through Zoom. Tutorials will be available on campus, live through scheduled Zoom sessions, and as prerecorded videos. Information regarding enrolments for these options will be provided during O-week, prior to the start of the semester.


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. Centrally scheduled examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further


Assessment Task 1

Value: 10 %
Due Date: 18/03/2022
Return of Assessment: 01/04/2022
Learning Outcomes: 1,2

Assignment 1

The assignment will require the use of R (and RStudio) to analyse real data and then to summarise and report the findings of the analysis. Students are expected to complete this assignment individually. This compulsory assignment is designed to focus on materials from Weeks 1 to 3, including material from the requisite regression course. The assignment will be submitted via Turnitin.

Assessment Task 2

Value: 30 %
Due Date: 06/05/2022
Return of Assessment: 29/05/2022
Learning Outcomes: 1,2,3,4,5

Assignment 2

The assignment will require the use of R (and RStudio) to analyse real data and then to summarise and report the findings of the analysis. Students are expected to complete this assignment individually. This compulsory assignment is designed to focus on materials from Weeks 4-7. The assignment will be submitted via Turnitin.

Assessment Task 3

Value: 60 %
Due Date: 10/06/2022
Return of Assessment: 30/06/2022
Learning Outcomes: 1,2,3,4,5,6,7

Final Project - Data Analysis and Competition

This compulsory project is designed to apply many of the statistical learning ideas you have been introduced throughout the course and requires the use of R 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. The 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 11 and will be due during the second week of the examination period (three week period for the final project). Specifically:

Release date 2022-05-20. Due date 2022-06-10.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. At its heart, academic integrity is about behaving ethically, committing to honest and responsible scholarly practice and upholding these values with respect and fairness.

The ANU commits to assisting all members of our community to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to be familiar with the academic integrity principle and Academic Misconduct Rule, uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with.

The Academic Misconduct Rule is in place to promote academic integrity and manage academic misconduct. Very minor breaches of the academic integrity principle may result in a reduction of marks of up to 10% of the total marks available for the assessment. The ANU offers a number of online and in person services to assist students with their assignments, examinations, and other learning activities. Visit the Academic Skills website for more information about academic integrity, your responsibilities and for assistance with your assignments, writing skills and study.

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

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

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

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

Thursday 13:30 14:30
Thursday 13:30 14:30
Dr Anton Westveld
+612 6125 5122?

Research Interests

Dr Anton Westveld

Thursday 13:30 14:30
Thursday 13:30 14:30

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