University of Technology Sydney

420104 Artificial Intelligence for Enterprises

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Information Technology: Computer Science
Credit points: 6 cp

Subject level:


Result type: Grade and marks

There are course requisites for this subject. See access conditions.

Recommended studies:

basic skills in Python programming; 430031 Python Programming for Data Processing is recommended for students who intend on taking 420104 Artificial Intelligence for Enterprises


This subject uses data analytics, data mining and knowledge discovery methods and their application to practical problems. It brings together the state-of-the-art research and practical techniques in data analytics, providing students with the necessary knowledge and capacity to initiate and conduct data mining research and development projects, and professionally communicate with analytics experts. Case studies allow engagement with issues of ethics, privacy and socio-cultural limitations of AI solutions.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1. Identify cultural and historical contexts and privacy relevant to Aboriginal and Torres Strait Islanders’ data. (A.1)
2. Design an appropriate AI project in a competitive business environment. (C.1)
3. Reflect on the risks and relevance of the socio-cultural impacts of AI in business. (B.1)
4. Apply machine learning techniques to an enterprise data set. (D.1)

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the development of the following Course Intended Learning Outcomes (CILOs):

  • Indigenous Professional Capability: FEIT graduates are culturally and historically well informed, able to co-design projects as respectful professionals when working in and with Aboriginal and Torres Strait Islander communities. (A.1)
  • Socially Responsible: FEIT graduates identify, engage, and influence stakeholders, and apply expert judgment establishing and managing constraints, conflicts and uncertainties within a hazards and risk framework to define system requirements and interactivity. (B.1)
  • Design Oriented: FEIT graduates apply problem solving, design thinking and decision-making methodologies in new contexts or to novel problems, to explore, test, analyse and synthesise complex ideas, theories or concepts. (C.1)
  • Technically Proficient: FEIT graduates apply theoretical, conceptual, software and physical tools and advanced discipline knowledge to research, evaluate and predict future performance of systems characterised by complexity. (D.1)

Teaching and learning strategies

This subject is made up of six modules delivered online over six weeks. Students work through each module at their own pace and momentum is maintained through weekly interactive activity attached to each theme and/or concept within the modules. Within each online module, content will be delivered through a mixture of short video presentations, interactive worksheets, animated learning materials, questions/activities, use cases and short summary/comprehension/annotation exercises for selected readings and concepts using CANVAS. Interactive activities throughout the modules will provide students with the opportunity to apply their understanding to practical use cases and engage with their peers. Over the six weeks, there will also be 3 synchronous one hour online interactive sessions, facilitated by teaching staff, that discuss the module, and provide opportunities for task-based group activity, discussion, feedback on learning progress and Q & A sessions. The assessment tasks build on each other in a way that feedback for earlier tasks can inform later tasks.

Content (topics)

Module 1: Introduction to AI in business

  • Business requirements and challenges in the digital age
  • Strengths and weaknesses of AI in a business context
  • The potential of AI to tackle business problems

Module 2: All those data…

  • The basic data set terminology
  • The key steps of data pre-processing
  • Data visualisation techniques

Modules 3 & 4: Machine learning hands on and interactive demos

  • Key definitions and structure of machine learning approaches
  • Supervised learning – classifications and hands-on practice
  • Unsupervised learning – classifications and hands-on practice
  • Other learning schemes
  • The world of interactive demos and systems

Module 5: The power of AI to achieve competitive advantage

  • The competitive environment and strategic priorities of a business
  • Finding the right AI project
  • Towards an AI roadmap

Module 6: The socio-cultural context of AI solutions

  • Implications of AI models for stakeholders in business and society
  • Societal challenges related to application of AI in business
  • Indigenous perspectives related to societal challenges of application of AI


Assessment task 1: A use case for AI in business


This assessment item focusses on the initial identification of a use case for AI in a business environment. Businesses are facing a variety of challenges in the digital age and AI can be a possible solution to tackle those challenges. This assessment task provides you with an opportunity to apply your understanding of these considerations to a specific case.


This assessment task addresses the following subject learning objectives (SLOs):


This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):


Type: Report
Groupwork: Individual
Weight: 15%

800 words (+/-10%)

Assessment task 2: Machine Learning Proof-of-Concept


This assessment item evaluates your learning towards the data collection, feature engineering, design, evaluation, and execution of a data analytical model, providing appropriate context-based recommendations. In this assessment, you evaluate the listed skills in three main themes of machine learning techniques, namely, unsupervised learning, supervised learning, and learning by experience.


This assessment task addresses the following subject learning objectives (SLOs):


This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):


Type: Project
Groupwork: Individual
Weight: 45%

3000 words report (+/-10%) & hands-on notebook of model results

Assessment task 3: AI Roadmap


This assessment task aims at consolidating previous findings into a compelling business proposal. It builds on the initial exploration in assessment task 1 and the technical modelling in assessment task 2 and puts those previous findings into the context of competitive advantage for the business in focus. You can reflect on specific business as well as wider societal impacts of AI and to develop their findings into a compelling narrative addressed at a senior business audience.


This assessment task addresses the following subject learning objectives (SLOs):

1, 2, 3 and 4

This assessment task contributes to the development of the following Course Intended Learning Outcomes (CILOs):

A.1, B.1, C.1 and D.1

Type: Presentation
Groupwork: Individual
Weight: 40%

Slide deck (around 10 slides) and 15 min video submission of pitch presentation.

Minimum requirements

In order to pass the subject, a student must achieve an overall mark of 50% or more.