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INFT71-326: Statistical Learning and Regression Models

Description

This subject covers the theory and practice of modern statistical learning, regression and classification modelling. Techniques covered range from traditional model selection and generalised linear model structures to modern, computer-intensive methods including generalised additive models, splines and tree methods. Methods to handle continuous, ordinal and nominal response variables and assessment of fit via cross-validation and residual diagnostics are also considered.  All techniques will be investigated via practical application on real data using the statistical software package R.

Subject details

TypePostgraduate
CodeINFT71-326
EFTSL0.125
FacultyBond Business School
Semesters offered
  • September 2019 [Standard Offering]
  • May 2020 [Standard Offering]
  • September 2020 [Standard Offering]
  • May 2021 [Standard Offering]
Credit10
Study areas
  • Business and Commerce
Subject fees
  • Commencing in 2019: $4,760
  • Commencing in 2020: $4,930

Learning outcomes

1. Demonstrate advanced knowledge of the limitations of linear regression models and the ability to develop an appropriate regression model.
2. Evaluate and choose between a variety of regression models.
3. Demonstrate advanced knowledge of the role of regularisation and the ability to use the concept to develop a variety of regression models.
4. Apply regression models for limited dependent variables (i.e., binomial, ordered and count data).
5. Apply generalised linear models, including proper use and assessment of model diagnostic techniques.
6. Develop regression models utilising splines, additive models and tree-based methods.
7. Correctly and concisely communicate the results and implications of a regression analysis in a professional written report.

Enrolment requirements

Requisites: ?

Nil

Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Assumed Prior Learning (or equivalent):

Possess demonstrable knowledge in the theory and application of simple and multiple linear regression models to the level of a unit such as ECON12-200 Econometrics as well as basic data science concepts and techniques to the level of a unit such as INFT71-216 Data Science

Restrictions: ?

Nil

Subject outlines

Subject dates

Standard Offering
Enrolment opens18/03/2019
Semester start13/05/2019
Subject start13/05/2019
Cancellation 1?27/05/2019
Cancellation 2?03/06/2019
Last enrolment26/05/2019
Withdraw – Financial?08/06/2019
Withdraw – Academic?29/06/2019
Teaching census?07/06/2019
Standard Offering
Enrolment opens14/07/2019
Semester start09/09/2019
Subject start09/09/2019
Cancellation 1?23/09/2019
Cancellation 2?30/09/2019
Last enrolment22/09/2019
Withdraw – Financial?05/10/2019
Withdraw – Academic?26/10/2019
Teaching census?04/10/2019