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 a variety of 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

Where possible, topics will be related to current research problems and reflect real world situations to emphasize the use of the techniques covered.

 

Additional Course Costs

The only other additional course costs are a calculator, textbook (if purchased) and printing materials.

Examination Material or equipment

There is no examination in this course. Please see Assessment sections for details and required material.

 

Required Resources

Class materials, including detailed lecture notes, slides, lecture demonstrations, tutorials, assignments and other relevant materials, will be made available on the class web page on Wattle. It is essential that you visit the class Wattle site regularly.

Recommended Resources

Recommended Text

James, G., Witten, D., Hastie, T., & Tibshirani, R. An introduction to statistical learning. First Edition or Second Edition. Springer.

A free ebook copy of the textbook is available at: https://statlearning.com/

Staff Feedback

Students will be given feedback (through both verbal and written comments) in the following forms in this course:

• To the whole class during lectures.

• Within tutorials.

• Individually during consultation hours.

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	Introduction to statistical learning and getting to know R.
2	Review of linear regression. Lectures and tutorials.
3	Classification. Lectures and tutorials.	Assignment 1 open
4	Classification. Lectures and tutorials.
5	Resampling methods. Lectures and tutorials.	Assignment 1 due
6	Linear model selection and regularisation I. Lectures and tutorials.
7	Introduction to unsupervised learning I. Linear model selection and regularisation II. Lectures and tutorials.
8	Moving beyond linearity. Lectures and tutorials.
9	Moving beyond linearity. Lectures and tutorials.	Assignment 2 open
10	Tree-based methods. Lectures and tutorials.	Final project open
11	Introduction to unsupervised learning II. Lectures and tutorials.	Assignment 2 due
12	Various topics of interest (e.g., generalised additive models, support vector machines, etc). Lectures and tutorials.

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 %	20/03/2024	28/03/2024	1,2,3
Assignment 2	25 %	15/05/2024	24/05/2024	1,2,3,4,5
Final Project	60 %	05/06/2024	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

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 Misconduct Rule before the commencement of their course. Other key policies and guidelines include:

• Student Assessment (Coursework) Policy and Procedure
• Special Assessment Consideration Policy and General Information 
• Student Surveys and Evaluations
• Deferred Examinations
• Student Complaint Resolution Policy and Procedure

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.

Participation

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.

Examination(s)

There is no examination in this course.

Assessment Task 1

Assignment 1

Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials of Weeks 1-4. Assignments may include derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in class and on Wattle. Assignments are expected to be in a PDF or Word file.

Estimated return date: The week after submission.

Assessment Task 2

Assignment 2

Turnitin submission. The students are expected to complete this assignment individually. This assignment is built based on materials of Weeks 1-10. Assignments may include derivation and application problems. The assignment questions will be released two weeks before the due date. The notification about access to the assignment will also be announced in class and on Wattle. Assignments are expected to be in a PDF or Word file.

Estimated return date: The week after submission.

Assessment Task 3

Final Project

Turnitin submission. The students are expected to complete this project individually. This final project will be based on all the materials covered throughout the duration of the semester. Students will be provided with further details regarding the final project in Week 10. This project requires the use of R to analyse real data. This project is designed to apply all the materials introduced in this course to analyse real datasets assigned by the course convener, as well as to predict some on-hold data. Written reports for this project (10 pages maximum for the main manuscript and 20 pages maximum for the appendix based on the format below, and all the R code should be relegated to the appendix) are expected to be submitted via Turnitin. Turnitin similarity check will be conducted for all the submitted reports.

Report Format – PDF or Word Upload

Use Australian English spelling. All pages (uploaded in PDF or Word form) must be as follows:

• Black type, or occasional coloured type for highlighting purposes;

• Single column;

• White A4 size paper with at least 0.5 cm margin on each side, top and bottom;

• Text must be size 12 point Times New Roman or an equivalent size before converting to PDF format and must be legible to assessors;

• References and appendices only can be in 10 point Times New Roman or equivalent.

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

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. 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).

• ANU Health, safety & wellbeing for medical services, counselling, mental health and spiritual support
• ANU Diversity and inclusion 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 and Learning Centre supports you make your own decisions about how you learn and manage your workload.
• ANU Counselling Centre promotes, supports and enhances mental health and wellbeing within the University student community.
• ANUSA supports and represents undergraduate and ANU College students
• PARSA supports and represents postgraduate and research students