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

The final exam will be in-person and closed-book. No dictionaries are allowed.

Required Resources

R (free to download)

The course makes extensive use of the free R software for statistical computing. PC/Mac labs are located at many places around campus: for an exhaustive list, visit the Information Commons link at

https://services.anu.edu.au/information-technology/software-systems

Students should be able to use their student cards to access these computing laboratories and should have a computer account automatically set up for them upon registration for this course. If you have not registered for the course, your card will not allow you access to the lab. To get started with the computing requirements for the course, students should make sure that they read the document "Introductory R Worksheet 1: PC familiarity" (linked to from the R workshop page, a link to which appears on the class home page). This document describes how you can log in to the PC’s. This document will also tell you how you can obtain or view other documents that you will find useful to learn about the computing setup for the course.

From the web page, you will be able to print out all of the lecture notes, all of the computer code used in the class, and all the tutorials and solutions.

No handouts will be made available except on the class web page.

A number of data sets will be analysed during lectures, live using R as much as possible. To assist you in understanding the data analyses, the R code used to produce displays discussed in class will be made available to you on the class web page. You are free to use and modify this code in conducting your own analyses.

Lecture Notes (available on Canvas). Class materials, including detailed lecture notes, workshop materials, tutorials, assignments and other relevant materials, will be made available on the class web page hosted on Canvas at https://canvas.anu.edu.au . To log on to Canvas, you need to have an ANU ID (your student number) and a password (the same as for obtaining your e-mail). In order to access the class web page within Canvas, you will need to be formally enrolled in the course. The class web page will be updated with new information on a regular basis, and will also contain links to other places of interest (such as an R workshop initially). It is essential that you visit the class web page regularly.

Reference materials

Resources (research papers, etc) and lecture notes will be provided throughout the semester on Canvas.

Recommended 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). 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	Overview of Supervised and Unsupervised Learning
2	Advanced Shrinkage Methods
3	Basis Expansions and Regularisation	Assessment 1 Due (8 August 4:00pm)
4	Kernel Smoothing Methods
5	Model Inference and Averaging
6	Additive Models, Trees and Related Methods	Assessment 2 Due (29 August 4:00pm)
7	Boosting and Additive Trees
8	Neural Network and Deep Learning
9	Support Vector Machine and Flexible Discriminants	Assessment 3 Due (3 October 4:00pm)
10	Advanced Unsupervised Learning Methods
11	Ensemble Learning
12	Undirected Graphical Models

Tutorial Registration

Tutorials commence in Week 2 of the semester and will be held on campus. ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more 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 %	08/08/2025	22/08/2025	1,2,3
Assignment 2	15 %	29/08/2025	12/09/2025	3,4,5
Assignment 3	15 %	03/10/2025	17/10/2025	2,4,5,6
Final Exam	55 %	30/10/2025	27/11/2025	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

Policies

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:

• Academic Integrity Policy and Procedure
• Student Assessment (Coursework) Policy and Procedure
• Extenuating Circumstances Application
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure
• Code of practice for teaching and learning

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 ‘Canvas’ 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.

Participation

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

Examination(s)

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

Assignment 1

This assessment is to be done individually. This assessment will be a take-home assessment that will typically involve a mix of ‘pen-and-paper’ questions and/or ‘computational’ questions. This assessment will match the style of questions given in the tutorials, so practicing on those questions would be quite beneficial. The question(s) will cover material that has been seen in previous lectures and are aimed at ensuring students are routinely studying the material. It should be submitted in Canvas via Turnitin. The assessment will be released two weeks before the due date. The assessment will be submitted in Canvas using TurnitIn by 4:00pm on the due date and marks/feedback will be given on the 'Return of Assessment Date'.

Assessment Task 2

Assignment 2

This assessment is to be done individually. This assessment will be a take-home assessment that will typically involve a mix of ‘pen-and-paper’ questions and/or ‘computational’ questions. This assessment will match the style of questions given in the tutorials, so practicing on those questions would be quite beneficial. The question(s) will cover material that has been seen in previous lectures and are aimed at ensuring students are routinely studying the material. It should be submitted in Canvas via Turnitin. The assessment will be released two weeks before the due date. The assessment will be submitted in Canvas using TurnitIn by 4:00pm on the due date and marks/feedback will be given on the 'Return of Assessment Date'.

Assessment Task 3

Assignment 3

This assessment is to be done individually. This assessment will be a take-home assessment that will typically involve a mix of ‘pen-and-paper’ questions and/or ‘computational’ questions. This assessment will match the style of questions given in the tutorials, so practicing on those questions would be quite beneficial. The question(s) will cover material that has been seen in previous lectures and are aimed at ensuring students are routinely studying the material. It should be submitted in Canvas via Turnitin. The assessment will be released two weeks before the due date. The assessment will be submitted in Canvas using TurnitIn by 4:00pm on the due date and marks/feedback will be given on the 'Return of Assessment Date'.

Assessment Task 4

Final Exam

This is a 2-hour exam that covers all course content and is worth 55% of the total assessment. The questions will provide practical methodologies under some scenarios and require students to give analytical comments or suggestions. No dictionaries allowed. The final exam will be held during the exam period with details to be advised no later than teaching week 10 of the semester. The Final exam will be held on campus and in person. 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. 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

All assessment submission in the course is online.

Late Submission

Late submission of assignments will incur a penalty of 5% per working day. Further, any late assessment tasks will not to 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.

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. Any use of artificial intelligence must be properly referenced. Failure to properly cite use of Generative AI will be considered a breach of academic integrity.

Returning Assignments

Assignments will be graded and returned on Canvas via TurnitIn. Students are encouraged to use the consultation time of the tutor and lecturer to obtain further feedback if necessary.

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

Not applicable.

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).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Accessibility for students with a disability or ongoing or chronic illness
• ANU Dean of Students for confidential, impartial advice and help to resolve problems between students and the academic or administrative areas of the University
• ANU Academic Skills supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents all ANU students