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,
Sem 1 2025

MATH2446

RMIT University Vietnam

Postgraduate

171H School of Science

Face-to-Face

 Viet1 2023

Course Coordinator: Dr. Shuwen Hu

Course Coordinator Phone: -

Course Coordinator Email: shuwen.hu@rmit.edu.au

Course Coordinator Availability: By appointment and email


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: 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer. 

Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning. 

Please follow the link for further information on how to apply for credit for prior study or experience. 


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. 

The course focuses 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 program learning outcomes for the following program(s):   

MC242 - Master of Analytics

PLO 1 Personal and Professional Awareness

  • the ability to contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • 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. 

PLO 2 Knowledge and Technical Competence

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

PLO 3 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. 

PLO 4 Teamwork and project management

  • the ability to contribute to professional work settings through effective participation in teams and organisation of project tasks
  • the ability to constructively engage with other team members and resolve conflict. 

PLO 5 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. 

PLO 6 Information Literacy 

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

PLO 7 Ethics

  • develop the cognitive skills to review critically, analyse, consolidate and synthesise knowledge to identify and provide solutions to complex problems with intellectual independence.
  • use initiative and judgement in planning, problem solving and decision making in professional practice and/or scholarship.
  • take responsibility and accountability for own learning and professional practice and in collaborations with others within broad parameters 

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


On completion of this course, you will be able to:  

  1. Evaluate the advanced concepts of machine learning, including the underlying assumptions and its limitations, in practical problem-solving scenarios.
  2. Critique the application of machine learning algorithms in complex real-world data analysis tasks, integrating relevant theories and methodologies.
  3. Evaluate the implementation of machine learning techniques on real datasets, using relevant software packages to assess their effectiveness
  4. Appraise the performance of different methods for a given machine learning problem.
  5. Articulate the results of machine learning algorithms to both specialist and non-specialist audiences in the format of a scientific report. 


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: Group Project 
Weighting 35% 
This assessment supports CLOs 1, 2, 3, 4 & 5 

Assessment Task 2: Bi-Weekly Quizzes 
Weighting 20% 
This assessment supports CLOs 1, 2 & 4 

Assessment Task 3: Final In-Class Test 
Weighting 40% 
This assessment supports CLOs 1, 2, 3 & 4 

Assessment Task 4: Weekly Practicals
Weighting 5%
This assessment supports CLOs 1, 2 & 3 

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.