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:
- Understand the rationale behind the formulation and components of a statistical model.
- Compare and contrast statistical models in the context of a particular scientific question.
- Communicate complex statistical ideas to a diverse audience.
- Formulate a statistical solution to real-data research problems.
- Understand the theoretical and computational underpinnings of various statistical procedures, including common classes of statistical models.
- Demonstrate computational skills to implement various statistical procedures.
Indicative Assessment
- Typical assessment may include, but is not restricted to: assignments, project and a final exam. (null) [LO null]
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
Majors
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 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2019 | $3840 |
- International fee paying students
| Year | Fee |
|---|---|
| 2019 | $5460 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
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 |
|---|---|---|---|---|---|---|
| 3991 | 25 Feb 2019 | 04 Mar 2019 | 31 Mar 2019 | 31 May 2019 | In Person | View |
