Part A: Course Overview

Course Title: Statistical Learning

Credit Points: 12.00


Course Coordinator: Prof Inge Koch

Course Coordinator Phone: +61 3 9925 0519

Course Coordinator Email: inge.koch@rmit.edu.au

Course Coordinator Location: 15.04.13

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

Recommended Prior Study

You should have satisfactorily completed or received credit for the following course/s before you commence this course:

Alternatively, you may be able to demonstrate the required skills and knowledge before you start this course.

Contact your course coordinator if you think you may be eligible for recognition of prior learning.


Course Description

This course introduces fundamental concepts and contemporary methods of Statistical Learning – component analysis and dimension reduction, supervised and unsupervised approaches, and shows how to apply these methods to different data science domains (e.g. physical sciences, medical and biological sciences, engineering, business and social sciences). Focus will be on the interaction between methods and data, on learning to choose suitable methods of data analysis for particular data and on interpreting the results. Statistical computing will form an essential part of this course. 


Objectives/Learning Outcomes/Capability Development

This course contributes to the program learning outcomes for the following program(s): 

BP350 - Bachelor of Science (Statistics Major)

PLO 1 Apply a broad and coherent knowledge of scientific theories, principles, concepts and practice in one or more scientific disciplines.
PLO 2 Analyse and critically examine scientific evidence using methods, technical skills, tools and emerging technologies in a range of scientific activities.
PLO 3 Analyse and apply principles of scientific inquiry and critical evaluation to address real-world scientific challenges and inform evidence based decision making.

For more information on the program learning outcomes for your program, please see the program guide.  


Upon successful completion of this course, you will be able to:  

  1. Construct and interpret visual presentations of data using appropriate software; 
  2. Choose appropriate methods, including checking the applicability of the underlying assumption of the method, for analysing high-dimension data; 
  3. Analyse high-dimension data using appropriate software and visualisations and interpret results of the analyses; 
  4. Communicate orally and in written form results of data analyses to peers and the community. 


Overview of Learning Activities

You will be actively engaged in a range of learning activites including lectorials and practicals. This course will be supported with digital content including recorded lectures and flexible learning resources organised into learning modules to provide the knowledge for the successful completion of assessments. Throughout the course you will be able to work both independently and in groups to aid development of your computational skills and statistical knowledge to apply to real-world data. 

You will be expected to participate in online classes and online discussions to contribute to the learning experiences of your student colleagues. 

You are encouraged to be proactive and self-directed in your learning, asking questions of your lecturer and/or peers and seeking out information as required, especially from the numerous sources available through the RMIT library, and through links and material specific to this course that is available through myRMIT Studies Course. 


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course. 

A list of recommended books complementing the course material of this course is provided on Canvas. 

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal. 


Overview of Assessment

Assessment Tasks 

Assessment Task 1: Multiple Choice Quizzes 
Weighting 10%
This assessment task supports CLOs 1, 2 & 3  

 Assessment Task 2: Group Assessment Reports  
Weighting 45%
This assessment task supports CLOs 1, 2, 3 & 4 

 Assessment Task 3: Invigilated in-Class End of Semester Test 
Weighting 45%
This assessment task supports CLOs 1, 2, 3 & 4

If you have a long term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.