Course Title: The AI Professional
Part A: Course Overview
Course Title: The AI Professional
Credit Points: 12.00
Please note that this course may have compulsory in-person attendance requirements for some teaching activities.
To participate in any RMIT course in-person activities or assessment, you will need to comply with RMIT vaccination requirements which are applicable during the duration of the course. This RMIT requirement includes being vaccinated against COVID-19 or holding a valid medical exemption.
Please read this RMIT Enrolment Procedure as it has important information regarding COVID vaccination and your study at RMIT: https://policies.rmit.edu.au/document/view.php?id=209.
Please read the Student website for additional requirements of in-person attendance: https://www.rmit.edu.au/covid/coming-to-campus
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.
171H School of Science
Sem 1 2020,
Sem 1 2021
RMIT University Vietnam
175H Computing Technologies
Course Coordinator: Mr. Tomas Turek
Course Coordinator Phone: -
Course Coordinator Email: email@example.com
Course Coordinator Location: -
Course Coordinator Availability: By appointment, by email
Pre-requisite Courses and Assumed Knowledge and Capabilities
We live in an era of rapid technological change which will increasingly see intelligent/data-driven computer systems take over control and decision making in diverse areas from business, law and security to healthcare and food production. This blended style course aims to equip you with the philosophical and ethical foundation needed by AI/Data Science professionals who will be at the forefront of transformation through AI and Data Science.
The role of an AI Professional and Data Scientist is an increasingly ubiquitous job role in many organisations of varying size. As well as a variety of technical skills, the practicing AI/Data Scientist also requires knowledge and awareness of key ethical, privacy and governance considerations for managing data, and data security and related techniques for implementing such. Moreover, a Data Scientist in an organisation must be an effective communicator both with other business units and with management, displaying an ability to both understand business needs and to communicate analyses effectively to inform decision making.
This course will: introduce concepts related to legal, ethical, privacy, governance issues for data collections; describe techniques for effectively managing such issues; provide you with techniques for effective communication and other considerations required of the AI practitioner or Data Scientist in a professional organisation.
The course will also: introduce the history of AI and Data Science, fundamental philosophical issues; consider current ethical issues in AI and building a responsible data science practice; explore the wider legal, social and economic impacts of widescale deployment of intelligent systems, and teach students to be innovative thinkers, leveraging the available datasets for social good.
Objectives/Learning Outcomes/Capability Development
Program Learning Outcomes
The course contributes to the program learning outcomes for MC271 – Master of Artificial Intelligence:
PLO 1: Enabling Knowledge
- Demonstrate mastery of a body of knowledge that includes recent developments in Artificial Intelligence, Computer Science and information technology
- Understand and use appropriate and relevant, fundamental and applied AI knowledge, methodologies and modern computational tools
- Recognise and use research principles and methods applicable to Artificial Intelligence
PLO 2: Critical Analysis
- Analyse and model complex requirements and constraints for the purpose of designing and implementing software artefacts and IT systems
- Evaluate and compare designs of software artefacts and IT systems on the basis of organisational and user requirements
- Bring together and flexibly apply knowledge to characterise, analyse and solve a wide range of AI problems
PLO 3: Problem Solving
- Design and implement software solutions that accommodate specified requirements and constraints, based on analysis or modelling or requirements specification
- Apply an understanding of the balance between the complexity / accuracy of the Artificial techniques used and the timeliness of the delivery of the solution
PLO 4: Communication
- Interpret abstract theoretical propositions, choose methodologies, justify conclusions and defend professional decisions to both IT and non-IT personnel via technical reports of professional standard and technical presentations.
PLO 5: Team Work
- Work effectively in different roles, to form, manage, and successfully produce outcomes from collaborative teams, whose members may have diverse cultural backgrounds and life circumstances, and differing levels of technical expertise.
PLO 6: Responsibility
- Effectively apply relevant standards, ethical considerations, and an understanding of legal and privacy issues to designing AI software, applications and IT systems
- Reflect on experience and improve your own future practice
- Locate and use data and information and evaluate its quality with respect to its authority and relevance
PLO 7: Research and Scholarship
- Demonstrate mastery of theoretical knowledge and to reflect critically on theory and professional practice or scholarship
- Plan and execute a substantial research-based project, capstone experience and/or piece of scholarship
Upon successful completion of this course you should have gained an understanding of the philosophical, ethical and legal issues related to AI and Data Science system deployment.
You should be able to:
- CLO1 Apply relevant standards, ethical, social, privacy, and governance considerations, and thus demonstrate an understanding of issues related to the practice of an AI and Data Science professional.
- CLO2 Analyse and discuss social impact and professional issues related to AI and Data Science (DS) and deployment of AI and DS systems. In particular, evaluate the implications of delegating control and decision making to AI and DS systems, including issues on fairness, bias, transparency, accountability and explainability of AI and DS systems.
- CLO3 Analyse and evaluate professional practice case studies, and to assess the work of peers
CLO4 Communicate effectively to a variety of audiences through a range of modes and media, specifically, through written technical reports and oral presentations
CLO5 Use research principles and choose appropriate methods to analyse, theorise and justify conclusions about new situations in AI and DS professional practice and/or research
Overview of Learning Activities
The learning activities included in this course are:
- Key concepts will be provided online (mix of text, audios, videos).
- Workshop sessions are interactive and discussion based, where students will be put into small groups to analyse existing AI and Data Science case studies.
- There will also be group presentation and peer review during the workshop sessions. These allow exploration of concepts with teaching staff and peers, and give feedback on students' progress and understanding.
- Students are expected to go through the given materials before the lectorials, as well as conducting self-study to work through and research beyond the course as presented in classes and learning materials.
- Assignments, as described in Overview of Assessment, requiring an integrated understanding of the subject matter.
Overview of Learning Resources
You will be able to access course information and learning materials and any recommended textbooks online linked through the Canvas learning management system. The course is divided into 12 weeks. The materials will be made available to you through Canvas each week prior to the classes. Lists of relevant reference texts, resources in the library and freely accessible online materials will also be provided.
Overview of Assessment
The assessment for this course comprises individual and group assignments, including a major project, presentations and peer reviews, as well as a list of Micro Creds to complete. Each of the assessment items requires you to demonstrate your knowledge of theoretical concepts, including application to new situations. Additionally, the assignments require you to research a scenario/problem, analyse it based on a professional framework, evaluate solutions, apply relevant standards, and create a professional report. Peer review involves oral and written feedback on the presentations delivered by other teams, as well as report(s) written by other students. The final project requires students to ideate, develop, and present an AI/Data Science-based idea and approach to a grand challenge faced by the society.
Note: This course has no hurdle requirements.
Assessment Task 1: AI/Data Science case study (group)
This assessment task supports CLOs 1,2,3,4,5
Assessment Task 2: AI/Data Science for social good project (group)
This assessment task supports CLOs 1, 2, 3, 4, 5
Assessment Task 3: Interviews and reflections (individual)
This assessment task supports CLOs 1, 2, 3, 4, 5
Assessment Task 4: Micro-Credentials
This assessment task supports CLOs 2, 4