• Class Number 3991
• Term Code 2930
• Class Info
• Unit Value 6 units
• Mode of Delivery In Person
• COURSE CONVENER
• Dr Yanrong Yang
• LECTURER
• Dr Yanrong Yang
• Class Dates
• Class Start Date 25/02/2019
• Class End Date 31/05/2019
• Census Date 31/03/2019
• Last Date to Enrol 04/03/2019
• TUTOR
• Daning Bi
• Yixin Li
SELT Survey Results

Statistical Learning (STAT3040)

This course provides an introduction to statistical learning and aims to develop skills in modern statistical data analysis. 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.  This course will cover a range of topics in statistical learning including linear regression, classification techniques, resampling methods (e.g., the bootstrap), regularisation methods, tree based methods, and unsupervised learning techniques (e.g., clustering). As the extensive use of statistical software is integral to modern data analysis, there will be a strong computing component in this course.

## Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

1. Understand the rationale behind the formulation and components of a statistical model.
2. Compare and contrast statistical models in the context of a particular scientific question.
3. Communicate complex statistical ideas to a diverse audience.
4. Formulate a statistical solution to real-data research problems.
5. Understand the theoretical and computational underpinnings of various statistical procedures, including common classes of statistical models.
6. Demonstrate computational skills to implement various statistical procedures.

## Research-Led Teaching

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.

## Required Resources

Prescribed Text (not compulsory but highly recommended)

•  Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani. (2013). An Introduction to Statistical Learning (with applications in R). Springer Texts in Statistics.

The text is available at Hancock library and has been request to be placed in 2 hour loan.

Other Recommended Text

•  Trevor Hastie, Robert Tibshirani, Jerome Friedman. (2008). The Elements of Statistical Learning (Data Mining, Inference and Prediction). 2nd Edition. Springer Series in Statistics.

The text is available at Hancock library and has been request to be placed in 2 hour loan.

## Staff Feedback

Students will be given feedback in the following forms in this course:

• Verbal communication from lecturers and tutors, individually upon request.

• Marks and summaries for the 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

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

In assignments and exams, students must appropriately reference any results, words or ideas that they take from another source which is not their own. A guide can be found at: https://academicskills.anu.edu.au/resources/handouts/referencing-basics .

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/ .

Assessment Requirements

Assessment for this course consists of three assignments and one final exam as detailed above and below. Attendance and participation in lectures and tutorials is not assessable.

STAT3040 is co-taught with STAT4040 and STAT7040. STAT4040 and STAT7040 students will have slightly different learning outcomes and assessment. They will also be allocated to separate tutorial groups. Tutorial questions will be the same for both cohorts.

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.

Extensions and Penalties

?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.

## Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to Statistical Learning
2 Some Techniques: Cross-validation and Bootstrap
3 Supervised Learning (1): Linear Regression---Shrinkage Methods
4 Supervised Learning (2): Nonlinear Regression---Local Polynomial Methods Assignment 1 (15%)
5 Supervised Learning (3[1]): Classification---Logistic Regression; K-Nearest Neighbors
6 Supervised Learning (3[2]): Classification---Discriminant Analysis
7 Supervised Learning (3[3]): Classification----Support Vector Machine
8 Tree-based Approaches for Supervised Learning Problems Assignment 2 (15%)
9 Unsupervised Learning (1): Principal Component Analysis (PCA)
10 Applications of PCA: Factor Model and Principal Component Regression (PCR)
11 Unsupervised Learning (2): Clustering Methods: K-Means Clustering and Hierarchical Clustering
12 Applications of Clustering: Homogeneity Pursuit in Big Data Assignment 3 (15%)

## Tutorial Registration

Please see Wattle for tutors information.

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.

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 Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Assignment 1 15 % 17/03/2019 24/03/2019 1,2,3,4,5,6
Assignment 2 15 % 28/04/2019 05/05/2019 1,2,3,4,5,6
Assignment 3 15 % 24/05/2019 28/05/2019 1,2,3,4,5,6
Final Exam 55 % 06/06/2019 04/07/2019 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

## 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. Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

## 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.

## Participation

Attendance and participation in lectures and tutorials is not assessable.

## Examination(s)

The final exam will be open book, with permitted materials being a "Non-programmable calculator" and otherwise "No restrictions", apart from items excluded by general ANU examinations policy (such as mobile phones). Students should check them prior to each exam, at http://timetable.anu.edu.au/ .

Value: 15 %
Due Date: 17/03/2019
Return of Assessment: 24/03/2019
Learning Outcomes: 1,2,3,4,5,6

Assignment 1

This assignment is an individual work, which covers linear regression and nonlinear regression. It consists of 6 short questions.

It is worth 15% of the total assessment. The assignment will be released in Week 1 and due in Week 4. It will be returned to students by Week 5.

Value: 15 %
Due Date: 28/04/2019
Return of Assessment: 05/05/2019
Learning Outcomes: 1,2,3,4,5,6

Assignment 2

This assignment will be a project related to classification methods. It requests you finish a report on a research topic.

It is worth 15% of the total assessment. The assignment will be released in Week 4 and due in Week 8. It will be returned to students in week 9.

Value: 15 %
Due Date: 24/05/2019
Return of Assessment: 28/05/2019
Learning Outcomes: 1,2,3,4,5,6

Assignment 3

This assignment will be a project related to unsupervised learning. It requests you finish a report on a research topic. It is worth 15% of the total assessment. The assignment will be released in Week 8 and due in Week 12. It will be returned to students within 4 days.

Value: 55 %
Due Date: 06/06/2019
Return of Assessment: 04/07/2019
Learning Outcomes: 1,2,3,4,5,6

Final Exam

This exam covers all knowledge and is worth 55% of the total assessment. The exam will be in the final examination period (with the exact date and venue to be announced on the official site http://www.anu.edu.au/students/program-administration/assessments-exams/examination-timetable).

## 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) as 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

Through Turnitin.

## Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. The Course Convener may grant extensions 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.

## 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.
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.

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).

## Convener

 Dr Yanrong Yang 6125 8975 yanrong.yang@anu.edu.au

### Research Interests

High dimensional statistics; large dimensional random matrix theory; large panel data analysis.

### Dr Yanrong Yang

 Friday 10:00 12:00 Friday 10:00 12:00

## Instructor

 Dr Yanrong Yang 6125 8975 yanrong.yang@anu.edu.au

### Dr Yanrong Yang

 Friday 10:00 12:00 Friday 10:00 12:00

## Tutor

 Daning Bi daning.bi@anu.edu.au

### Daning Bi

 Wednesday 11:00 12:30

## Tutor

 Yixin Li 61259045 Yixin.Li@anu.edu.au

### Yixin Li

 Thursday 16:30 17:00