Course Title: Foundations of AI for STEM

Part A: Course Overview

Course Title: Foundations of AI for STEM

Credit Points: 12.00

Terms

Course Code

Campus

Career

School

Learning Mode

Teaching Period(s)

COSC2959

City Campus

Postgraduate

171H School of Science

Face-to-Face

Sem 2 2021

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 science, technology, engineering, math, and medicine students. AI is a branch of computer science devoted to devising intelligent machinery. AI is a large field with an exciting history. Applications of AI are ubiquitous, transforming many fields in science, technology, engineering, and health, and society. It is therefore imperative for everyone to develop an understanding of the foundations and applications of the field of AI relevant to their own discipline.


Objectives/Learning Outcomes/Capability Development

This course is an option course for several programs and a core course in GC197 Graduate Certificate in Artificial Intelligence contributing to the following Program Learning Outcomes:

PLO 1: Enabling Knowledge
You will gain skills as you apply knowledge with creativity and initiative to new situations. In doing so, you will recognise some principles and methods applicable to Artificial Intelligence.

PLO 3: Problem Solving
You will begin to acquire the capability to analyse problems and synthesise suitable solutions as you learn to design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification.

PLO 4: Communication
You will begin to learn to communicate effectively with a variety of audiences through a range of modes and media, in particular to interpret propositions, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.


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

CLO1 Explain the foundations and applications of Artificial Intelligence (AI) in Science, Technology, Engineering, and Mathematics (STEM).

CLO2 Identify, analyze and solve real-world STEM problems using AI approaches, algorithms and applications.

CLO3 Evaluate ethical and safety considerations in the development and deployment of AI applications in STEM.

CLO4 Communicate accurately and collaborate effectively using a variety of tools and techniques specific to the fields of AI and STEM.


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 segment of the course offers a compulsory module, namely Introduction to AI. 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 fourth segment of the course offers a compulsory module, namely, Ethics, Safety, and Future.


Overview of Learning Resources

You should make extensive use of computer laboratories and relevant software provided by the School. You should 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.

Details of 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