Course Title: Machine Learning

Part A: Course Overview

Course Title: Machine Learning

Credit Points: 12.00

Important Information:

Please note that this course may have compulsory in-person attendance requirements for some teaching activities. 

Terms

Course Code	Campus	Career	School	Learning Mode	Teaching Period(s)
COSC2673	City Campus	Undergraduate	171H School of Science	Face-to-Face	Sem 2 2018, Sem 2 2019, Sem 1 2020, Sem 1 2021
COSC2673	City Campus	Undergraduate	175H Computing Technologies	Face-to-Face	Sem 1 2022, Sem 1 2023, Sem 1 2024
COSC2753	RMIT University Vietnam	Undergraduate	171H School of Science	Face-to-Face	Viet1 2019, Viet1 2020, Viet1 2021
COSC2753	RMIT University Vietnam	Undergraduate	175H Computing Technologies	Face-to-Face	Viet1 2022, Viet1 2023, Viet1 2024
COSC2812	RMIT Vietnam Hanoi Campus	Undergraduate	175H Computing Technologies	Face-to-Face	Viet1 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:

COSC2627 - Discrete Structures in Computing (Course ID 049804)
OR
MATH2411 - Mathematics for Computing 1 (Course ID 054076)
OR
MATH1150 - Discrete Mathematics (Course ID 008610)
OR
MATH2394 - Engineering Mathematics (Course ID 053543)
OR
COSC2129 - Artificial Intelligence (Course ID 004123)

AND

COSC1076 / COSC2082 / COSC2136 / COSC2696 - Advanced Programming Techniques (Course ID 004068)
OR
COSC2802 - Programming Bootcamp 2 (Course ID 054080)
OR
COSC2815 - Advanced Programming in Python (Course ID 054117)
OR
COSC2288 / COSC2391 / COSC2440 / COSC2684 / COSC2786 - Further Programming (Course ID 014052)
OR
EEET2482 - Software Engineering (Course ID 038296)
OR
COSC3054 - Programming Bootcamp 1 (Course ID 054079)
OR
COSC2800 - IT Studio 2 (Course ID 054075)

Successful completion of [add course code(s) and title(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 your information go to RMIT Course Requisites webpage. 

Course Description

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. Students 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. Students will perform assignments that involve a variety of real world datasets from a variety of domains.

 

Objectives/Learning Outcomes/Capability Development

This course contributes to the following Program Learning Outcomes for BP094 Bachelor of Computer Science, BP096 Bachelor of Software Engineering, BP214 Bachelor of Information Technology (Games and Graphics Programming):

• PLO1: Knowledge - Apply a broad and coherent set of knowledge and skills for developing user-centric computing solutions for contemporary societal challenges.
  
  
• PLO2: Problem Solving - Apply systematic problem solving and decision-making methodologies to identify, design and implement computing solutions to real world problems, demonstrating the ability to work independently to self-manage processes and projects.
  
  
• PLO4: Communication - Communicate effectively with diverse audiences, employing a range of communication methods in interactions.to both computing and non-computing personnel.
  
  
• PLO6: Responsibility and Accountability - Demonstrate integrity, ethical conduct, sustainable and culturally inclusive professional standards, including First Nations knowledges and input in designing and implementing computing solutions.

 

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 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, Canvas, 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

Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

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

Assessment Task 2: Practical &Written Assignment (group/individual)
Weight: 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