• Class Number 7461
  • Term Code 3160
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Yanrong Yang
    • Dr Yanrong Yang
  • Class Dates
  • Class Start Date 26/07/2021
  • Class End Date 29/10/2021
  • Census Date 14/09/2021
  • Last Date to Enrol 02/08/2021
SELT Survey Results

This course offers an introduction to modern statistical approaches for complicated data structures, and is designed for students who need to do advanced statistical data analyses and statistical research. There has been a prevalence of “big data” in many different scientific fields. In order to tackle the analysis of data of such size and complexity, traditional statistical methods have been reconsidered and new methods have been developed for extracting information, or "learning", from such data. Due to the wide of range of topics which could be considered, this course, each offering, will cover only a few of the potential topics. Some of the topics that may be considered are: regularisation and dimension reduction, clustering and classification, non-independent data, and causality. Emphasis is placed on methodological understanding, empirical applications, as well as theoretical foundations to a certain degree. As the extensive use of statistical software is integral to modern data analysis, there will be a strong computing component in this course.

Learning Outcomes

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

  1. Describe the rationale behind the formulation and components of complex statistical models.
  2. Compare and contrast statistical models in the context of a variety of scientific questions.
  3. Communicate complex statistical ideas to a diverse audience.
  4. Formulate a statistical solution to real-data research problems.
  5. Demonstrate an understanding of the theoretical and computational underpinnings of various statistical procedures, including common classes of statistical models.
  6. Utilise computational skills to implement various statistical procedures.

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

You will require reliable access to Wattle during the final exam.

Required Resources

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

This textbook is available in library and the link is https://library.anu.edu.au/record=b2370538. The electronic version is open and free on the website https://web.stanford.edu/~hastie/Papers/ESLII.pdf .

Staff Feedback

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

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

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.

Class Schedule

Week/Session Summary of Activities Assessment
1 Overview of Supervised and Unsupervised Learning
2 Advanced Shrinkage Methods
3 Basis Expansions and Regularisation Assignment 1 (10%)
4 Kernel Smoothing Methods
5 Model Inference and Averaging
6 Additive Models, Trees and Related Methods
7 Boosting and Additive Trees Assignment 2 (15%)
8 Neural Network and Deep Learning
9 Support Vector Machine and Flexible Discriminants
10 Advanced Unsupervised Learning Methods
11 Ensemble Learning Assignment 3 (15%)
12 Undirected Graphical Models

Tutorial Registration

Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. Information regarding enrolments for these options will be provided on Wattle during O-week of the semester.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 10 % 13/08/2021 20/08/2021 1,2,3
Assignment 2 15 % 24/09/2021 01/10/2021 3,4,5
Assignment 3 15 % 22/10/2021 29/10/2021 2,4,5,6
Final Exam 60 % 04/11/2021 02/12/2021 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 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.


Course content delivery will take the form of weekly live (Zoom) lectures and weekly tutorials, delivered in hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos).


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

Assessment Task 1

Value: 10 %
Due Date: 13/08/2021
Return of Assessment: 20/08/2021
Learning Outcomes: 1,2,3

Assignment 1

This assignment is an individual work, which covers shrinkage methods. It consists of 6 short questions.

It is worth 10% of the total assessment and this assignment is not redeemable. It should be submitted in Wattle via Turnitin. The assignment will be released on 2021-07-26 and due at 23:59pm on 2021-08-13. It will be returned to students on 2021-08-20.

Assessment Task 2

Value: 15 %
Due Date: 24/09/2021
Return of Assessment: 01/10/2021
Learning Outcomes: 3,4,5

Assignment 2

This assignment is an individual work, which will be a project related to model inference and averaging. It requests you finish a report on a research topic.

It is worth 15% of the total assessment and this assignment is not redeemable. It should be submitted in Wattle via Turnitin. The assignment will be released on 2021-08-13 and due at 23:59pm on 2021-09-24. It will be returned to students on 2021-10-01.

Assessment Task 3

Value: 15 %
Due Date: 22/10/2021
Return of Assessment: 29/10/2021
Learning Outcomes: 2,4,5,6

Assignment 3

This assignment is an individual work, which will be a project related to advanced unsupervised learning. It requests you finish a report on a research topic.

It is worth 15% of the total assessment and this assignment is not redeemable. It should be submitted in Wattle via Turnitin. The assignment will be released on 2021-09-24 and due at 23:59pm on 2021-10-22. It will be returned to students on 2021-10-29.

Assessment Task 4

Value: 60 %
Due Date: 04/11/2021
Return of Assessment: 02/12/2021
Learning Outcomes: 1,2,3,4,5,6

Final Exam

This exam covers all knowledge and is worth 60% 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 questions will provide practical methodologies under some scenarios and require students to give analytical comments or suggestions. There is an additional question to final exam questions of STAT3050. The final examination will be a take-home exam during the university examination period at the end of semester. The final examination will be 4 hour long and cover the entire syllabus. It will be open book and all materials are permitted. The exam will be centrally timetabled and details of the final examination timetable will be made available on the ANU Timtabling website.

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

Late submission is not permitted. If submission of assessment tasks without an extension after the due date is not permitted, 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

Research Interests

Yanrong Yang - High Dimensional Statistical Inference, Large Panel Data Analysis, Large Dimensional Random Matrix Theory, Functional Data Analysis

Dr Yanrong Yang

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

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