Course Title: Machine Learning

Part A: Course Overview

Course Title: Machine Learning

Credit Points: 12.00


Course Coordinator: Dr Vural Aksakalli

Course Coordinator Phone: +61 3 9925

Course Coordinator Email: @rmit.edu.au

Course Coordinator Location: 08.09.084

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

MATH2200 Introduction to Probability and Statistics

MATH2201 Basic Statistical Methodologies


Course Description

Machine Learning involves automatically identifying patterns in data to suggest future predictions about a task. The explosion of data in different fields, such as health and finance, and in sources such as social media, has made Machine Learning an increasingly core Computer Science competency, with many companies investing in data analytics and the world’s major IT companies (such as Google, Facebook, and others) establishing Machine Learning labs.

This course will introduce the basic Machine Learning concepts, covering supervised and unsupervised techniques, evaluation, as well as specific approaches such as deep neural networks.


Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP083 Bachelor of Applied Mathematics and Statistics:

PLO1. 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.
PLO2. Knowledge and technical competence • an understanding of appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools.
PLO3. Problem-solving • 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.   PLO4. Teamwork and project management • the ability to constructively engage with other team members and resolve conflict.   Communication • 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 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. 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 course project. The course will be supported by the Canvas learning management system.  


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 http://rmit.libguides.com/mathstats    


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Course Project (submitted in two phases)

Weighting 25%
This assessment task supports CLOs 1, 2, 3 & 4

Assessment 2: Mid-semester Test
Weighting 25%
This assessment task supports CLOs 1, 2 & 4.

Assessment 3: Final Exam
Weighting 50% 
This assessment supports CLOs 1, 2 & 4.