Course Title: Machine Learning

Part A: Course Overview

Course Title: Machine Learning

Credit Points: 12.00


Course Code




Learning Mode

Teaching Period(s)


City Campus


171H School of Science


Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020

Course Coordinator: Dr Vural Aksakalli

Course Coordinator Phone: +61 3 9925 2277

Course Coordinator Email:

Course Coordinator Location: 15.04.03

Course Coordinator Availability: By appointment and email

Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH1324 - Introduction to Statistics or Equivalent

MATH2267 - Essential Mathematics for Analytics or Equivalent


Course Description

As time permits, this course will cover the following topics:

  • Data preparation for machine learning
  • Information-based learning
  • Similarity-based learning
  • Probability-based learning
  • Feature selection and feature ranking
  • Model evaluation
  • Clustering
  • Case studies

The course will be delivered using the Python programming language and the Scikit-Learn machine learning module in a Jupyter Notebook environment.

Objectives/Learning Outcomes/Capability Development


This course contributes to the following Program Learning Outcomes for MC004 Master of Statistics and Operations Research and MC242 Master of Analytics:

Personal and professional awareness

  • the ability to reflect on experience and improve your own future practice
  • the ability to apply the principles of lifelong learning to any new challenge.

Knowledge and technical competence

  • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.


  • the ability to bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of problems
  • an understanding of the balance between the complexity / accuracy of the mathematical / statistical models used and the timeliness of the delivery of the solution.

Teamwork and project management

  • the ability to constructively engage with other team members and resolve conflict.


  • the ability to effectively communicate both technical and non-technical material in a range of forms (written, electronic, graphic, oral) and to tailor the style and means of communication to different audiences. Of particular interest is the ability to explain technical material, without unnecessary jargon, to lay persons such as the general public or line managers.

Information literacy

  • the ability to locate and use data and information and evaluate its quality with respect to its authority and relevance.

On completion of this course you will be able to:

  1. Understand the fundamental concepts of machine learning, the underlying assumptions, and its limitations.
  2. Develop a thorough understanding of popular machine learning algorithms.
  3. Perform efficient implementation of these techniques on real data using the relevant software packages.
  4. Assess and compare performance of different methods for a given machine learning problem.

Overview of Learning Activities

The course will be delivered through a combination of face-to-face lectures and computer practice sessions. While attendance at weekly lectures is beneficial, there is an expectation that you will spend more time out of class on this course, in particular on the tests and the course project. The course will be fully supported by the Canvas learning management system.


Total study hours

You will undertake 3 hours face-to-face learning every week through a combination of lectures and computer practice sessions. It is expected that you will commit a minimum of 4-6 hours per week out-of-class in independent & collaborative study.

Overview of Learning Resources

A list of prescribed/ recommended textbooks for this course will be provided on Canvas. All course materials will be posted on Canvas.

The software packages can be accessed from the school computer labs, as well as through the RMIT MyDesktop system anywhere anytime.

Library Subject Guide for Mathematics & Statistics  

Overview of Assessment

This course has no hurdle requirements.


Assessment Tasks

Task 1: In-semester tests

Weight: 32% (two online-submission tests with 16% each)

This assessment supports CLOs 1, 2, 3, and 4.


Task 2: Course project

Weight: 18%

This assessment supports CLOs 1, 2, 3, and 4


Task 3: Final exam

Weight: 50% 

This assessment supports CLOs 1, 2, and 4