Part A: Course Overview

Course Title: Machine Learning

Credit Points: 12.00

Important Information:

 

 


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

MATH2387

City Campus

Undergraduate

171H School of Science

Face-to-Face

 Sem 2 2021,
Sem 1 2023,
Sem 1 2025

Course Coordinator: Dr Zhendong Huang

Course Coordinator Phone: -

Course Coordinator Email: zhendong.huang@rmit.edu.au

Course Coordinator Availability: By appointment, by 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

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 analytical 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. 

The course will be delivered using the Python programming language and libraries, such as Scikit-Learn, in a Jupyter Notebook environment. 


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.
PLO 4 Communicate, report and reflect on scientific findings, to diverse audiences utilising a variety of formats employing integrity and culturally safe practices.  

BP083P23 - Bachelor of Applied Mathematics and Statistics (Statistics Major) 

PLO 1 Apply a broad and coherent knowledge of mathematical and statistical theories, principles, concepts and practices with multi-disciplinary collaboration.  
PLO 2 Analyse and critically examine the validity of mathematical and statistical arguments and evidence using methods, technical skills, tools and computational technologies.  
PLO 3 Formulate and model real world problems using principles of mathematical and statistical inquiry to inform evidence-based decision making. 

BP083P20 - Bachelor of Science (Applied Mathematics and Statistics) 

PLO 1 Personal and Professional Awareness

  • contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • reflect on experience and improve your own future practice
  • apply the principles of lifelong learning to any new challenge. 

PLO 2 Knowledge and Technical Competence

  • use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools 

PLO 4 Teamwork and Project Management 

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

BH101AMS – Bachelor of Science (Dean’s Scholar, Applied Mathematics and Statistics) (Honours) 

PLO 1 Personal and professional awareness 

  • contextualise outputs where data are drawn from diverse and evolving social, political and cultural dimensions
  • reflect on experience and improve your own future practice
  • apply the principles of lifelong learning to any new challenge. 

PLO 2 Knowledge and technical competence 

  • use the appropriate and relevant, fundamental and applied mathematical and statistical knowledge, methodologies and modern computational tools. 

PLO 3 Problem-solving 

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

PLO 4 Teamwork and project management 

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

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. Reconstruct fundamental concepts of machine learning, including underlying assumptions and limitations, in practical problem-solving scenarios..
  2. Analyse the application of machine learning algorithms in real-world data analysis tasks.
  3. Perform the implementation of machine learning techniques on real datasets using relevant software packages
  4. Review 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 computer practical sessions. The course will be supported by the Canvas learning management system. We will make heavy use of Canvas, so you need to check regularly for important Canvas announcements. You should also monitor discussion forums on Canvas on a regular basis to benefit from the questions and answers posted in there.    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 prescribed and recommended textbooks for this course will be provided on Canvas. All course materials will be posted on Canvas, including lecture notes, computer practice materials, assessment details, teaching schedule, and staff contact details.    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. 
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: Group Project  
Weighting 35%
This assessment supports CLOs 1, 2, 3, and 4 

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

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

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