Course Title: Machine Learning

Part A: Course Overview

Course Title: Machine Learning

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2319

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 1 2017,
Sem 1 2018,
Sem 1 2019,
Sem 1 2020,
Sem 1 2021,
Sem 1 2023,
Sem 1 2024

MATH2446

RMIT University Vietnam

Postgraduate

171H School of Science

Face-to-Face

Viet1 2023

Course Coordinator: Dr. Devindri Perera

Course Coordinator Phone: +61 3 9925 0396

Course Coordinator Email: devindri.perera@rmit.edu.au

Course Coordinator Availability: By appointment and email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

You should have satisfactorily completed 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

Modern organisations (be it financial, educational, health, or any other business organisations) generate, and collect massive amounts of data. These data can hold significant amounts of information into the functioning of the organisation, and their analysis will help formulate policies for future growth. To be of use to the organisation, this data must be analysed to extract insights that can help to make better decisions for the organisation. Machine Learning is defined as an automated process that extracts such patterns from data. 

This course will introduce basic Machine Learning concepts and will focus mainly on supervised machine learning techniques. Supervised machine learning techniques automatically learn a model of the relationship that exists between the descriptive features and a target feature of the data, and will be based on a set of historical (existing) examples or instances of data.  

In this course we will focus on data preparation, training of models, and the evaluation of models. 

We will be focusing on  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. 

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. 

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 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 lectorials and practice sessions. The course will be fully supported by the Canvas learning management system. 

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.

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: Course Project
Weighting 40%
This assessment supports CLOs 1, 2, 3 & 4

Assessment Task 2: Bi-Weekly Quizzes
Weighting 40%
This assessment supports CLOs 1, 2, 3 & 4

Assessment Task 3: Online Final Test
Weighting 20%
This assessment supports CLOs 1, 2 & 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.