• 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

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.

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 STAT2008 or STAT2014, and have completed STAT2001 or STAT2013.

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.  

If you are a domestic graduate coursework or international student you will be required to pay tuition fees. Tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.

Student Contribution Band:
2
Unit value:
6 units

If you are an undergraduate student and 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). You can find your student contribution amount for each course 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
3212 24 Feb 2020 02 Mar 2020 31 Mar 2020 29 May 2020 In Person N/A

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