- Class Number 3212
- Term Code 3030
- Class Info
- Unit Value 6 units
- Mode of Delivery In Person
- Dr Tao Zou
- 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
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 classification techniques on qualitative response variables.
- Assess models based on resampling methods.
- Carry out model selection based on regularisation methods.
- Utilise tree-based methods.
- Perform basic 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.
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.
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.
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.
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.
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/
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.
|Week/Session||Summary of Activities||Assessment|
|1||Introduction to statistical learning and getting to know R.|
|2||Review of linear regression. Lectures and labs.||Release of Group Project on Wattle|
|3||Classification. Lectures and labs.|
|4||Classification. Lectures and labs.||Release of Assignment 1 on Wattle|
|5||Resampling methods. Lectures and labs.||Submission of Assignment 1|
|6||Linear model selection and regularisation I. Lectures and labs.||Feedback of Assignment 1|
|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.||Submission and Presentation of Group Project|
|10||Tree-based methods. Lectures and labs.||Release of Assignment 2 on Wattle|
|11||Introduction to unsupervised learning II. Lectures and labs.||Submission of Assignment 2|
|12||Various topics of interest (e.g., generalised additive models, support vector machines, etc). Lectures and labs.||Feedback of Group Project and Assignment 2|
Please see Wattle for tutors’ information.
In this course, tutorials are in the form of 1-hour computer labs for each week (starting from Week 2).
Tutorial signup for this course will be done via the Wattle website. Detailed information about signup times will be provided on Wattle. When tutorials are available for enrolment, follow these steps:
1. Log on to Wattle, and go to the course site.
2. Click on the link “Tutorial enrolment”.
3. On the right of the screen, click on the tab “Become Member of......” for the tutorial class you wish to enter.
4. Confirm your choice.
If you need to change your enrolment, you will be able to do so by clicking on the tab “Leave group...” and then re-enrol in another group. You will not be able to enrol in groups that have reached their maximum number. Please note that enrolment in ISIS must be finalised for you to have access to Wattle.
|Assessment task||Value||Due Date||Return of assessment||Learning Outcomes|
|Assignment 1||5 %||26/03/2020||03/04/2020||1,2,3|
|Group Project||15 %||07/05/2020||29/05/2020||1,2,3,4,5|
|Assignment 2||10 %||21/05/2020||29/05/2020||1,2,3,4,5|
|Final Exam||70 %||04/06/2020||02/07/2020||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 Misconduct Rule before the commencement of their course. Other key policies and guidelines include:
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.
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
Learning Outcomes: 1,2,3
Hard copy submission. The students are expected to complete this assignment individually. This assignment is designed to cover materials from Week 1 to 3. Assignments will require the use of R to analyse real data and then to summarise and report on the findings of the analysis. The assignment questions will be released before Monday of Week 4. The notification about access to the assignment will also be announced in Weeks 3-4 during lectures and on Wattle. Assignments are expected to be printed and contain relevant computer code and graphics.
Estimated return date: The week after submission.
Assessment Task 2
Learning Outcomes: 1,2,3,4,5
All the students will be formed into groups with three group members (generally student-selected, convener directed if necessary) to complete this project. The group member information needs to be submitted along with Assignment 1. This group project is designed to apply materials in this course to analyse a real dataset chosen by students themselves. The students will present the group project in the computer lab of Week 9, and the written presentation slides (pdf or ppt) need to be submitted through Turnitin also in Week 9. Note that all the students in a group need to present in the presentations (except for the students with proper documentations for absence), and both the lecturer and tutor will mark the oral presentations and written presentation slides for each group. The notification about the group project will also be announced in Week 2 during lectures and on Wattle. This group project requires the use of R to analyse real data.
Value: Presentation 12% + Slides 3% (see the following rubrics).
Estimated return date: Week 12.
Complete data science workflows were shown with a clear logic in the project.
Most of the data science workflows were shown in the project.
Only part of the data science workflows were shown in the project.
Majority of the data science workflows were left out and the logic to analyse the data was not clear.
Most of the findings based on the real data problem and data analysis were original.
Some of the findings based on the real data problem and data analysis were original.
Only one or two of the findings based on the real data problem and data analysis were original.
The findings based on the real data problem and data analysis were not original at all.
Statistical analysis in this course
The statistical analysis was used correctly and properly for the data. Methodology was relevant to this course.
The statistical analysis was used correctly, but was not perfectly appropriate for the data. Methodology was relevant to this course.
Some of the statistical analysis was used incorrectly, and was not perfectly appropriate for the data. Methodology was relevant to this course.
The statistical analysis was used incorrectly, and was not appropriate for the data. Methodology was irrelevant to this course.
Extensive knowledge to interpret the output.
Members showed complete understanding of statistical analysis. Accurately addressed the analysis based on the background of data.
Most showed a good interpretation of output.
All members able to addressed most of analysis based on data.
Few members showed good interpretation of some parts of the analysis.
Only some members accurately addressed the analysis based on data.
Presenters didn’t understand the data analysis.
Majority of output interpreted by only one member or majority of information incorrect.
The slides were a concise summary of the data analysis with all the above categories. Comprehensive and complete coverage of information. Relevant sources referenced.
The slides were a good summary of the data analysis.
Most important information covered; little irrelevant info. Relevant sources referenced.
The slides were informative but several elements went unclear.
Much of the information irrelevant; coverage of some of major points. Relevant sources partly referenced.
The slides were a brief look at the data analysis but many of the above categories were left unclear.
Majority of information irrelevant and significant points left out. Relevant sources not referenced.
Assessment Task 3
Learning Outcomes: 1,2,3,4,5
Hard copy submission. The students are expected to complete this assignment individually. This assignment is designed to cover materials from Week 4 to 9. Assignments will require the use of R to analyse real data and then to summarise and report on the findings of the analysis. The assignment questions will be released before Monday of Week 10. The notification about access to the assignment will also be announced in Weeks 9-10 during lectures and on Wattle. Assignments are expected to be printed and contain relevant computer code and graphics.
Estimated return date: The week after submission
Assessment Task 4
Learning Outcomes: 1,2,3,4,5,6,7
The final examination will be based on all the work covered throughout the duration of the semester. The final examination is worth 70% 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 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.
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.
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.
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.
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.
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
Financial statistics, time series analysis
Dr Tao Zou