- Class Number 2759
- Term Code 3330
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Yanrong Yang
- Dr Yanrong Yang
- Class Dates
- Class Start Date 20/02/2023
- Class End Date 26/05/2023
- Census Date 31/03/2023
- Last Date to Enrol 27/02/2023
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 in detail models based on resampling methods.
- Carry out model selection based on a variety of regularisation methods.
- Utilise tree-based methods.
- Perform unsupervised learning techniques.
Where possible, topics will be related to current research problems and reflect real-world situations to emphasize the use of the techniques covered.
Examination Material or equipment
Examination materials and conditions will be noticed to all students via Wattle and the examination office.
James, G., Witten, D., Hastie, T., & Tibshirani, R. (2nd Edition). An introduction to statistical learning. Springer.
The lecturer has requested that the library makes available as a 2 hour or 2 day loan.
The lecturer has requested that the campus bookstore makes the textbook available.
Supplementary Reading (Not Compulsory)
Hastie, T., Tibshirani, R., & Friedman, J. (2nd Edition). The elements of statistical learning: data mining, inference, and prediction. Springer.
This book is available in the library.
Students will be given feedback (through both verbal and written comments) in the following forms in this course:
• To the whole class during lectures and local tutorials.
• Individually during consultation hours.
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.
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 (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.
The University offers a number of support services for students. Information on these is available online from http://students.anu.edu.au/studentlife/.
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/
STAT4040/6040 will be taught jointly. There may be some material which is only relevant to some of these codes. This will be clearly identified during the lecture and/or tutorial. The different cohorts of students will also be treated separately in grading and any scaling that is applied.
|Week/Session||Summary of Activities||Assessment|
|1||Overview of Statistical Learning Readings: James et. al. Chapter 2|
|2||Resampling Methods: Cross-Validation and The Bootstrap Readings: James et. al. Chapter 5|
|3||Parametric Regression (1): The Least Squares' Viewpoint Readings: James et. al. Chapter 3|
|4||Parametric Regression (2): Regularisation Readings: James et. al. Chapter 6||Assignment 1 due Friday of Week 4|
|5||Classification (1): Logistic Regression and Discriminant Analysis Readings: James et. al. Chapter 4|
|6||Classification (2): Support Vector Classifiers Readings: James et. al. Chapter 9|
|7||Nonparametric Regression (1): Smoothing Splines and Local Regression Readings: James et. al. Chapter 7|
|8||Nonparametric Regression (2): Neural Networks and Deep Learning Readings: James et. al. Chapter 10||Assignment 2 due Friday of Week 8|
|9||Tree-Based Methods for Regression and Classification Readings: James et. al. Chapter 8|
|10||Unsupervised Learning (1): Principal Components Analysis and Factor Analysis Readings: James et. al. Chapter 12|
|11||Unsupervised Learning (2): Clustering Analysis Readings: James et. al. Chapter 12|
|12||Multiple Testing: The False Discovery Rate Readings: James et. al. Chapter 13||There will be a final exam during the university examination period. More information and instructions regarding the exam will be provided no later than week 10 on Wattle.|
Tutorials will be available on campus, live through scheduled Zoom sessions and as pre-recorded videos. Students should enrol in their tutorial using MyTimetable.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||15 %||17/03/2023||24/03/2023||1,4|
|Assignment 2||25 %||28/04/2023||05/05/2023||2,3,5|
|Final Exam||60 %||01/06/2023||29/06/2023||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
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.
Course content delivery will take the form of weekly on-campus lectures (recorded and available via echo360 on Wattle) and weekly tutorials, delivered in hybrid format (on campus, live through scheduled Zoom sessions and as pre-recorded videos). Weekly consultations with the lecturer and the tutor(s) will be conducted over Zoom.
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
Learning Outcomes: 1,4
This assignment is an individual work, which covers the contents of Week 1 and Week 2. It is worth 15% of the total assessment and this assignment is not redeemable. It should be submitted in Wattle via Turnitin. This assignment will be released at 3:00pm on 2023-02-24 and due at 23:59pm on 2023-03-17. It will be returned to students on 2023-03-24.
Assessment Task 2
Learning Outcomes: 2,3,5
This assignment is an individual work, which covers the contents of Weeks 3 - 6. It is worth 25% of the total assessment and this assignment is not redeemable. It should be submitted in Wattle via Turnitin. This assignment will be released at 3:00pm on 2023-03-24 and due at 23:59pm on 2023-04-28. It will be returned to students on 2023-05-05.
Assessment Task 3
Learning Outcomes: 1,2,3,4,5,6,7
This exam covers all knowledge and is worth 60% of the total assessment. 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 paper will consist of 10 qualitative and quantitative questions that require written solutions. The exam will be centrally timetabled and details of the final examination timetable will be made available on the ANU Timetabling website.
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 this 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.
Assignments will be returned to students 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 to resubmit assignments.
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
High-dimensional Statistics; Functional Time Series Analysis; Large-dimensional Random Matrix Theory; Large Portfolio Selection.
Dr Yanrong Yang