Course Title: Foundations of Artificial Intelligence for STEM

Part A: Course Overview

Course Title: Foundations of Artificial Intelligence for STEM

Credit Points: 12.00

Important Information:

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

Please check your Canvas course shell closer to when the course starts to see if this course requires mandatory in-person attendance. The delivery method of the course might have to change quickly in response to changes in the local state/national directive regarding in-person course attendance.


Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2960

City Campus

Undergraduate

171H School of Science

Face-to-Face

Sem 2 2021

COSC2960

City Campus

Undergraduate

175H Computing Technologies

Face-to-Face

Sem 1 2022,
Sem 2 2022

Course Coordinator: Dr Haytham Fayek

Course Coordinator Phone: +61 3 9925 0858

Course Coordinator Email: haytham.fayek@rmit.edu.au

Course Coordinator Location: 014.11.003

Course Coordinator Availability: By appointment


Pre-requisite Courses and Assumed Knowledge and Capabilities

None.


Course Description

This course introduces the foundations of Artificial Intelligence (AI) tailored to students from a range of health, science, technology, engineering, and math disciplines. AI is a branch of computer science devoted to developing intelligent hardware and software systems. Applications of AI are now widespread in the world of work. It is therefore increasingly important for all health, science, technology, engineering, and math disciplines graduates to have an understanding of the foundations and applications of the field of AI relevant to their own discipline. This course will also challenge you to consider the impact and ethics of AI on your future profession and society.


Objectives/Learning Outcomes/Capability Development

A basic understanding of the foundations and applications of AI as applied in contemporary health, science, technology, engineering, and math workplaces and future trends in applications from an inter-disciplinary perspective.


On successful completion of this course, the student will be able to:

  • CLO1 Explain the foundations and applications of Artificial Intelligence (AI) in the fields of Health, Science, Technology, Engineering, and Mathematics.
  • CLO2 Identify, analyse and solve real-world health, science, technology, engineering and mathematics problems using AI approaches, algorithms and applications.
  • CLO3 Explore ethical and safety considerations in the development and deployment of AI applications in health, science, technology, engineering and mathematics.
  • CLO4 Communicate accurately and collaborate effectively using a variety of tools and techniques specific to the fields of AI and health, science, technology, engineering and mathematics.


Overview of Learning Activities

This course follows a modular structure. The course comprises six modules. Modules vary in duration spanning two to four weeks. Two modules are compulsory, and two modules will be your choice (from a choice of four modules).

The first module “Introduction to AI” is compulsory. The second segment of the course offers a choice of two modules, namely, “Classical AI” and “Data Science”. The third segment of the course offers a choice of two modules, namely “Machine Learning” and “Applications of AI”. The final module “Ethics, Safety, and Future” is also compulsory.


Overview of Learning Resources

You will use software provided by the STEM College to complete some assessments.

All other resources will be provided on and through the course Canvas shell. Links to reference texts available online from the RMIT library as well as relevant internet sites are also provided.


Overview of Assessment

This course has no hurdle requirements.

Assessment Tasks

Assessment Task 1: Quizzes
Weighting 10%
This assessment task supports CLO 1

Assessment Task 2A/B: Assignment
Weighting 30%
This assessment task supports CLOs 2 & 3

Assessment Task 3A/B: Assignment
Weighting 30%
This assessment task supports CLOs 2 & 3

Assessment Task 4: Group Assignment
Weighting 30%
This assessment task supports CLOs 3 & 4