- Class Number 2843
- 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
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).
Upon successful completion, students will have the knowledge and skills to:
- Use packages and process output relating to statistical learning in the statistical computing package R.
- Fit linear and non-linear regression models and analyse relationships between a response variable and covariates.
- Perform in-depth classification techniques on qualitative response variables.
- Assess models based on resampling methods.
- Carry out model selection based on a variety of regularisation methods.
- Utilise tree-based methods.
- Perform unsupervised learning techniques.
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.
- James, Witten, Hastie,, and Tibshirani. 2021. An Introduction to Statistical Learning (second edition). Springer.
- The authors provide a free e-book for downloading at https://hastie.su.domains/ISLR2/ISLRv2_website.pdf
- 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//
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
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.
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.
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.
|Summary of Activities
|Introduction to Statistical Learning
|Regression - Review
|Assignment 1 is Released; Tutorials Begin
|Assignment 1 is Due
|Linear Model Selection and Regularisation
|Feedback for Assignment 1
|Moving Beyond Linearity
|Assignment 2 is Released
|Support Vector Machines
|Assignment 2 is Due
|Time Permitting: Deep Learning (or another topic)
|Final Project is Released
|Time Permitting: Deep Learning (or another topic)
|Feedback for Assignment 2
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
|Return of assessment
|Final Project - Data Analysis and Competition
* 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 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
- Special Assessment Consideration Guideline and General Information
- Student Surveys and Evaluations
- Deferred Examinations
- Student Complaint Resolution Policy and Procedure
- Code of practice for teaching and learning
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 ‘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
Learning Outcomes: 1,2
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 1 to 3, including material from the requisite regression course. The assignment will be submitted via Turnitin.
Assessment Task 2
Learning Outcomes: 1,2,3,4,5
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
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 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.
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.
There is no hardcopy submission in the course.
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.
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.
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.
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 Access 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
Research interests include Bayesian methodology and theory and statistical methods for interaction/relational data.
Dr Anton Westveld