• Class Number 4865
  • Term Code 3030
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery Online
  • COURSE CONVENER
    • Dr Tao Zou
  • Class Dates
  • Class Start Date 24/02/2020
  • Class End Date 05/06/2020
  • Census Date 08/05/2020
  • Last Date to Enrol 02/03/2020
  • TUTOR
    • Lingyu He
SELT Survey Results

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 classification techniques on qualitative response variables.
  4. Assess models based on resampling methods.
  5. Carry out model selection based on regularisation methods.
  6. Utilise tree-based methods.
  7. Perform basic 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

• Calculator (non-programmable).

• Unannotated paper-based dictionary (no approval required). Both English language dictionaries and translation dictionaries are permitted.

• Five A4 pages with notes on both sides. Both print and handwritten notes are permitted.

 

Required Resources

Recommended Text

James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). 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. (2009). The elements of statistical learning: data mining, inference, and prediction. Springer.


This book is available in the library.

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 labs.

• Individually during consultation hours.

Students will also be given group project feedback on Turnitin and written comments in the marked assignments.

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.

Other Information

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.

Scaling

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.

Referencing Requirements

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/

Assignment Submission

Hard Copy Submission for Two Assignments: Two assignments are submitted via the physical assignment box at the front of the admin office on Level 4, CBE Building (26C). The cover sheet must use the assignment cover sheet template. Assignments must include the cover sheet available on Wattle site. Please keep a copy of tasks completed for your records. Email and fax submissions are not acceptable.

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 labs.
3 Classification. Lectures and labs.
4 Classification. Lectures and labs.
5 Resampling methods. Lectures and labs.
6 Linear model selection and regularisation I. Lectures and labs.
7 Introduction to unsupervised learning I. Linear model selection and regularisation II. Lectures and labs.
8 Moving beyond linearity. Lectures and labs.
9 Moving beyond linearity. Lectures and labs.
10 Tree-based methods. Lectures and labs.
11 Introduction to unsupervised learning II. Lectures and labs.
12 Various topics of interest (e.g., generalised additive models, support vector machines, etc). Lectures and labs.

Assessment Summary

Assessment task Value Learning Outcomes
Final Exam 100 % 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:

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 ANU Online 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.

Examination(s)

Centrally administered examinations through Examinations, Graduations & Prizes will be timetabled prior to the examination period. Please check ANU Timetabling for further information. Further information about the examination will be provided in class and on Wattle closer to the time of the examination.

Assessment Task 1

Value: 100 %
Learning Outcomes: 1,2,3,4,5,6,7

Final Exam

The final examination will be based on all the work covered throughout the duration of the semester. The final examination is worth 100% of the final raw score. The exam will include a mixture of theoretical and numerical questions. Students will be provided with further details regarding the exam no later than the week 11 lectures.

Examination/Writing Time: 180 minutes.

Reading Time: 15 minutes.

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

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

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

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 group projects will be returned through Turnitin. The marked two hard copy assignments will be mainly returned to students via the admin office on Level 4, CBE Building (26C). Students will be provided with further details on Wattle site regarding the other returning information as it approaches. You should retain a copy of your submission for your own records. If you do not collect your assignments, they will be destroyed after the end of the semester.

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

Resubmission of assignments will not be accepted.

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).

Dr Tao Zou
61250487
enquiries.rsfas@anu.edu.au

Research Interests


Dr Tao Zou

Lingyu He
6125 0487
lingyu.he@anu.edu.au

Research Interests


Lingyu He

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions