• Offered by Rsch Sch of Finance, Actuarial Studies & App Stats
  • ANU College ANU College of Business and Economics
  • Course subject Statistics
  • Areas of interest Actuarial Studies, Statistics
  • Academic career PGRD
  • Course convener
    • Dr Anton Westveld
  • Mode of delivery In Person
  • Co-taught Course
  • Offered in First Semester 2022
    See Future Offerings

This course involves on campus teaching. For students unable to come to campus there will be a remote option. See the Class Summary for more details.

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

Indicative Assessment

  1. Assignments (30) [LO 1,2,3,4,5,6,7]
  2. Exams (70) [LO 1,2,3,4,5,6,7]

The ANU uses 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. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Workload

Students are expected to commit at least 10 hours per week to completing the work in this course. This will include at least 3 contact hours per week and up to 7 hours of private study time.

Inherent Requirements

Not applicable

Requisite and Incompatibility

To enrol in this course you must have completed STAT6039 or STAT6013 and have completed STAT6038 or STAT6014 or STAT7001. Incompatible with STAT3040 and STAT4040.

Prescribed Texts

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

Fees

Tuition fees are for the academic year indicated at the top of the page.  

Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees

Student Contribution Band:
1
Unit value:
6 units

If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Where there is a unit range displayed for this course, not all unit options below may be available.

Units EFTSL
6.00 0.12500
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

The list of offerings for future years is indicative only.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.

First Semester

Class number Class start date Last day to enrol Census date Class end date Mode Of Delivery Class Summary
2829 21 Feb 2022 28 Feb 2022 31 Mar 2022 27 May 2022 In Person N/A

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