Part A: Course Overview

Course Title: Computational Machine Learning

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2793

City Campus

Postgraduate

171H School of Science

Face-to-Face

 Sem 1 2020,
Sem 1 2021

COSC2793

City Campus

Postgraduate

175H Computing Technologies

Face-to-Face

 Sem 1 2022,
Sem 1 2023,
Sem 1 2024,
Sem 1 2025

COSC3013

RMIT University Vietnam

Postgraduate

175H Computing Technologies

Face-to-Face

 Viet2 2023,
Viet2 2024

Course Coordinator: Azadeh Alavi

Course Coordinator Phone: N/A

Course Coordinator Email: azadeh.alavi@rmit.edu.au

Course Coordinator Location: 14.08.06B

Course Coordinator Availability: Monday : 12:00 pm to 2:00 pm & Wednesday 9:00 am to 11:00 am


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-Requisite Courses

Successful completion of the following course/s:

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course.

For information go to RMIT Course Requisites webpage.

 

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

Computational Machine Learning involves automatically identifying patterns in data to suggest future predictions about a task: e.g., predicting future house prices from historical data and trends. 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. You will learn how to apply such techniques to a range of problems, using open source Machine Learning toolkits, and learn how to analyse outputs from the applications. You will perform assignments that involve a variety of real world datasets from a variety of domains.


Objectives/Learning Outcomes/Capability Development

Program Learning Outcomes

This course contributes to the program learning outcomes for the following program(s):

MC271 - Master of Artificial Intelligence

PLO 1    Enabling Knowledge
PLO 2    Critical Analysis
PLO 3    Problem Solving
PLO 4    Communication
PLO 6    Responsibility

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


On completion of this course you should be able to:

  1. Understand the fundamental concepts and algorithms of machine learning and applications   
  2. Understand a range of machine learning methods and the kinds of problem to which they are suited  
  3. Set up a machine learning configuration, including processing data and performing feature engineering, for a range of applications   
  4. Apply machine learning software and toolkits for diverse applications   
  5. Understand major application areas of machine learning   
  6. Understand the ethical considerations involved in the application of machine learning


Overview of Learning Activities

The learning activities included in this course are:

  • key concepts will be explained in pre-recorded lectures, classes or online, where syllabus material will be presented and the subject matter will be illustrated with demonstrations and examples;
  • tutorials and/or labs and/or group discussions (including online forums) focused on projects and problem solving will provide practice in the application of theory and procedures, allow exploration of concepts with teaching staff and other students, and give feedback on your progress and understanding;
  • assignments, as described in Overview of Assessment (below), requiring an integrated understanding of the subject matter; and
  • private study, working through the course as presented in classes and learning materials, and gaining practice at solving conceptual and technical problems.


Overview of Learning Resources

You are encouraged to bring your laptops and use the freely available software to conduct the laboratories.

You will make extensive use of computer laboratories and relevant software provided by the School. You will be able to access course information and learning materials through MyRMIT and may be provided with copies of additional materials in class or via email. Lists of relevant reference texts, resources in the library and freely accessible Internet sites will be provided.


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Task 1: Practical & Written Assignment (individual)
Weighting: 30%
This assessment task supports CLOs 1, 3, 4

Assessment Task 2: Practical &Written Assignment (group/individual)
Weighting: 50%
This assessment task supports CLOs 1, 3, 4, 6

Assessment Task 3: Virtual Presentation & Interview (individual)
Weight: 20 %
This assessment task supports CLOs 1, 2, 5, 6

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.