• Class Number 3230
  • Term Code 3030
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Yanrong Yang
    • Dr Yanrong Yang
  • Class Dates
  • Class Start Date 24/02/2020
  • Class End Date 05/06/2020
  • Census Date 08/05/2020
  • Last Date to Enrol 02/03/2020
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 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

This course provides the up-to-date introduction to modern statistical development. Apart from imparting of statistical techniques, applications in empirical studies are also illustrated.

Examination Material or equipment

No material is allowed for the final exam.

Required Resources

 Prescribed Text (not compulsory but highly recommended)

•  Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013). An Introduction to Statistical Learning (with applications in R). Springer Texts in Statistics.

The text is available at Hancock library and has been request to be placed in 2 hour loan. It is also available in Campus Bookstore.

Other Recommended Text

•  Trevor Hastie, Robert Tibshirani, Jerome Friedman. (2008). The Elements of Statistical Learning (Data Mining, Inference and Prediction). 2nd Edition. Springer Series in Statistics.

The text is available at Hancock library and has been request to be placed in 2 hour loan.

Staff Feedback

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

• Verbal communication from lecturers and tutors, individually upon request.

• Marks and summaries for the assignments.

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


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 (i.e. if your raw mark exceeds that of another student, then your scaled mark will exceed the scaled mark of that student), and may be either up or down.

Referencing Requirements

In assignments and exams, students must appropriately reference any results, words or ideas that they take from another source which is not their own. A guide can be found at: https://academicskills.anu.edu.au/resources/handouts/referencing-basics

 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/

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.

Extensions and Penalties

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.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to Statistical Learning
2 Some Techniques: Cross-validation and Bootstrap
3 Supervised Learning (1): Linear Regression---Shrinkage Methods Assignment 1 (15%)
4 Supervised Learning (2): Nonlinear Regression---Local Polynomial Methods
5 Supervised Learning (3[1]): Classification---Logistic Regression; K-Nearest Neighbors
6 Supervised Learning (3[2]): Classification---Discriminant Analysis
7 Supervised Learning (3[3]): Classification---Support Vector Machine Assignment 2 (15%)
8 Tree-based Approaches for Supervised Learning Problems
9 Unsupervised Learning (1): Principal Component Analysis (PCA)
10 Applications of PCA: Factor Model and Principal Component Regression (PCR)
11 Unsupervised Learning (2): Clustering Methods---K-Means Clustering and Hierarchical Clustering Assignment 3 (15%)
12 Applications of Clustering: Homogeneity Pursuit in Big Data

Tutorial Registration

Please see Wattle for tutors' information.

Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle. When tutorials are available for enrolment, follow these steps:

1. Log on to Wattle, and go to the course site.

2. Click on the link “Tutorial enrolment”

3. On the right of the screen, click on the tab “Become Member of ……” for the tutorial class you wish to enter.

4. Confirm your choice

If you need to change your enrolment, you will be able to do so by clicking on the tab “Leave group…” and then re-enrol in another group. You will not be able to enrol in groups that have reached their maximum number. Please note that enrolment in ISIS must be finalised for you to have access to Wattle.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 15 % 13/03/2020 20/03/2020 1,2,3,4
Assignment 2 15 % 24/04/2020 30/04/2020 1,2,4,6,7
Assignment 3 15 % 22/05/2020 29/05/2020 3,4,5,6,7
Final Exam 55 % 04/06/2020 02/07/2020 1,2,3,4,5,6

* 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 ANU Online 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.


?Attendance and participation in lectures and tutorials is not assessable.


The final exam will be closed-book exam, with no permitted materials. Students should check them prior to each exam, at http://timetable.anu.edu.au/.


Assessment Task 1

Value: 15 %
Due Date: 13/03/2020
Return of Assessment: 20/03/2020
Learning Outcomes: 1,2,3,4

Assignment 1

This assignment is an individual work, which covers linear regression and nonlinear regression. It consists of 6 short questions.

It is worth 15% of the total assessment. The assignment will be released in Week 1

and due in Week 3. It will be returned to students by Week 4.

Assessment Task 2

Value: 15 %
Due Date: 24/04/2020
Return of Assessment: 30/04/2020
Learning Outcomes: 1,2,4,6,7

Assignment 2

This assignment will be a project related to classification methods. It requests you finish a report on a research topic.

It is worth 15% of the total assessment. The assignment will be released in Week 3

and due in Week 7. It will be returned to students in week 8.


Assessment Task 3

Value: 15 %
Due Date: 22/05/2020
Return of Assessment: 29/05/2020
Learning Outcomes: 3,4,5,6,7

Assignment 3

This assignment will be a project related to unsupervised learning. It requests you finish a report on a research topic. It is worth 15% of the total assessment. The assignment will be released in Week 7 and due in Week 11. It will be returned to students in Week 12.

Assessment Task 4

Value: 55 %
Due Date: 04/06/2020
Return of Assessment: 02/07/2020
Learning Outcomes: 1,2,3,4,5,6

Final Exam

This exam covers all knowledge and is worth 55% of the total assessment. The exam will be in the final examination period (with the exact date and venue to be announced on the official site http://www.anu.edu.au/students/program-administration/assessments-exams/examination-timetable). The final exam will last 2.5 hours. The questions will provide practical methodologies under some scenarios and require students to give analytical comments or suggestions. Past exam papers (2018, 2019) are available.

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

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

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

Through Turnitin.

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 Yanrong Yang
6125 8975

Research Interests

High dimensional statistics; large dimensional random matrix theory; large panel data analysis.

Dr Yanrong Yang

Friday 14:00 16:00
Friday 14:00 16:00
Dr Yanrong Yang
6125 8975

Research Interests

Dr Yanrong Yang

Friday 14:00 16:00
Friday 14:00 16:00

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