Course Title: Machine Learning

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

Course Coordinator: Dr. Devindri Perera

Course Coordinator Phone: +61 3 9925 0396

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

Course Coordinator Location: 15.04.19

Course Coordinator Availability: By appointment, by email


Pre-requisite Courses and Assumed Knowledge and Capabilities

Required Prior Study

You should have satisfactorily completed courses 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

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 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 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. Evaluate fundamental concepts of machine learning, the underlying assumptions, and its limitations.
  2. Demonstrate application of popular machine learning algorithms.
  3. Perform efficient implementation of these techniques on real data using relevant software packages.
  4. Critically 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 practice 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.  


Overview of Learning Resources

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.
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  

Weighting 40%

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

Assessment Task 2: Bi-Weekly Quizzes

Weighting 40%

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

Assessment Task 3: Online Final Test  

Weighting 20%

This assessment supports CLOs 1, 2, and 4