- Class Number 6987
- Term Code 3360
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Yanrong Yang
- Dr Yanrong Yang
- Class Dates
- Class Start Date 24/07/2023
- Class End Date 27/10/2023
- Census Date 31/08/2023
- Last Date to Enrol 31/07/2023
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.
Upon successful completion, students will have the knowledge and skills to:
- Describe the rationale behind the formulation and components of a statistical model.
- Compare and contrast statistical models in the context of a particular scientific question.
- Communicate statistical ideas to a diverse audience.
- Formulate a statistical solution to real-data research problems.
- Demonstrate an understanding of the theoretical and computational underpinnings of various statistical procedures, including common classes of statistical models.
- Utilise computational skills to implement various statistical procedures.
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.
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 .
There are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.
ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.
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
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.
|Week/Session||Summary of Activities||Assessment|
|1||Overview of Supervised and Unsupervised Learning|
|2||Advanced Shrinkage Methods|
|3||Basis Expansions and Regularisation||Assignment 1 (15%)|
|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|
Tutorials will be available on campus. Students should enrol in their tutorial using MyTimetable. "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 task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||15 %||11/08/2023||18/08/2023||1,2,3|
|Assignment 2||15 %||22/09/2023||29/09/2023||3,4,5|
|Assignment 3||15 %||20/10/2023||27/10/2023||2,4,5,6|
|Final Exam||55 %||02/11/2023||30/11/2023||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 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, all delivered on campus. 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: 1,2,3
This assignment is an individual work, which covers shrinkage methods. It consists of 6 short questions.
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 2023-07-24 and due at 23:59pm on 2023-08-11. It will be returned to students on 2023-08-18. Assignment solutions will be available on 2023-08-25.
Assessment Task 2
Learning Outcomes: 3,4,5
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 2023-08-22 and due at 23:59pm on 2023-09-22. It will be returned to students on 2023-09-29. Assignment solutions will be available on 2023-10-06.
Assessment Task 3
Learning Outcomes: 2,4,5,6
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 2023-09-20 and due at 23:59pm on 2023-10-20. It will be returned to students on 2023-10-27. Assignment solutions will be available on 2023-10-29.
Assessment Task 4
Learning Outcomes: 1,2,3,4,5,6
This exam covers all knowledge 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. 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 exam will be centrally timetabled and details of the final examination timetable will be made available on the ANU Timtabling website. The exam will be on campus (in person) and the duration will be approximately 3 hours.
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.
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 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.
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.
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
Yanrong Yang - High Dimensional Statistical Inference, Large Panel Data Analysis, Large Dimensional Random Matrix Theory, Functional Data Analysis
Dr Yanrong Yang