Outline

Overview

ENGG2112 provides an introduction to the context of engineering practice and how engineers engage with other professions in concept development, analysis, and planning. Students are introduced to basic concepts in data science used by engineers to understand problems, support decision making, and run systems. Students will then work within teams to address components of a complex multi-disciplinary project relevant to their chosen engineering stream. In the process, students will consider the influence of contextual factors such as regulatory frameworks, economics, and societal expectations. In doing so, student teams will draw from various fields such as economics, law, business, and the social sciences as they complete the project.

Unit details and rules

Academic unit	Engineering
Credit points	6
Prerequisites ?	(INFO1110 or INFO1910 or ENGG1810) and (MATH1005 or MATH1905 or MATH1062 or MATH1962 or MATH1972 or BUSS1020) and (AERO1560 or BMET1960 or CHNG1108 or CIVL1900 or ELEC1004 or ELEC1005 or ENVE1001 or MECH1560 or MTRX1701)
Corequisites ?	None
Prohibitions ?	ENGG1111
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Nicholas TSE, nicholas.tse@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
In-person practical, skills, or performance task or test	A7. Tutorial Participation/Weekly Submission Verbal Defence Weekly updates each week to the tutor, justifying contributions to teamwork and individual work on the project. Due to the public holiday, the weeks that will be counted are W4,5,8,9,10,12	15%	Multiple weeks	5 minutes	AI prohibited
Outcomes assessed:
Evaluation	A4. Final Reflection Video Submission (Canvas or LinkedIn) Reflection on what was learned and how the project went. Video to be posted on LinkedIn or submitted via Canvas.	10%	STUVAC Due date: 06 Jun 2026 at 23:59 Closing date: 06 Jun 2026	1 page reflection with 1 min video	AI allowed
Outcomes assessed:
Practical skill	EFT: Particpation in Team work Forming a team in class, submission of relevant work on Canvas.	0%	Week 03	15 minutes	AI allowed
Outcomes assessed:
Experimental design	A1. Project Proposal (group mark, SPARKPLUS moderation) Description of project justification, workplan, objectives and deliverables. Individualised marks will be moderated by SPARKPLUS peer evaluation.	10%	Week 06	Lesser of 3 pages or 1000 words	AI allowed
Outcomes assessed:
In-person practical, skills, or performance task or test	A5. In-Class Quiz 1 Canvas quiz, lockdown browser	15%	Week 07	60 minutes	AI prohibited
Outcomes assessed:
In-person practical, skills, or performance task or test	A6. In-Class Quiz 2 Canvas quiz, lockdown browser	15%	Week 11	60 minutes	AI prohibited
Outcomes assessed:
Interactive oral	A3. Project Presentation (group mark, SPARKPLUS moderation) Group presentation of the project highlights; group mark will be moderated by SPARKPLUS peer evaluation.	15%	Week 13	15 minutes	AI prohibited
Outcomes assessed:
Written work	A2. Project Final Report (group mark, SPARKPLUS moderation) Final project report. Requirements will be discussed in class. Individual marks will be derived from the group mark and moderated by SPARKPLUS assessment.	20%	Week 13	Lesser of 10 pages or 5000 words.	AI allowed
Outcomes assessed:
= group assignment ? = early feedback task ?

Early feedback task

This unit includes an early feedback task, designed to give you feedback prior to the census date for this unit. Details are provided in the Canvas site and your result will be recorded in your Marks page. It is important that you actively engage with this task so that the University can support you to be successful in this unit.

Assessment summary

Quizzes: Short, low-weight multiple-choice quizzes run in class on Canvas using a lockdown browser. Quizzes cover topics and processes introduced before the week of the quiz and are designed to help you keep up with the unit content throughout the semester. More details will be provided on Canvas.

Presentations: Team-based oral presentations linked to the group project. Each team member is required to present, giving everyone the opportunity to practise clear, concise, and professional oral communication. More details will be provided on Canvas.

Reports: Written reports that focus on clear and concise communication for a knowledgeable lay audience. The two main written tasks are the project proposal and the final project report. More details will be provided on Canvas.

Group work and peer evaluation (SPARKplus): All group assessments include self- and peer-evaluation using SPARKplus. These evaluations are used to adjust individual marks from the group score. If SPARKplus is not completed, it will be assumed that all group members contributed equally and the unadjusted group mark will be applied. More details will be provided on Canvas.

Reflection Video Submission (Canvas or LinkedIn): A short reflection video where you discuss your project, what you learned, and the skills you developed. The task also supports employability by encouraging you to present and promote your work on LinkedIn. Students with privacy concerns should discuss options with the unit coordinator. More details will be provided on Canvas.

Weekly Tutorial Participation / Verbal Defence: Weekly tutorial activities where you explain and defend your contribution to the group project. This task develops your ability to clearly describe your work, engage with the subject content, and ask questions as needed. More details will be provided on Canvas.

Assessment criteria

Result Name	Mark Range	Description
High Distinction	85 – 100	When you have surpassed the required learning outcomes and shown initiative/effort well beyond what was expected
Distinction	75 – 84	When you show that you have achieved the learning outcomes to a very high level
Credit	65 – 74	When you demonstrate a more than adequate achievement of the learning outcomes
Pass	50 – 64	When you barely demonstrate achievement of the learning outcomes
Fail	0 – 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In line with University policy, submissions after the closing date will not be accepted. Any late submission must have approved Special Consideration. For quizzes, this may allow an alternative in-class sitting to be arranged with the tutor. The final presentation cannot be delayed or rescheduled. SPARKplus peer evaluation must be completed by the submission deadline; late or missing SPARKplus submissions will not be counted. If you expect any difficulty meeting a deadline, you should contact your tutor or the unit coordinator before the due date. Unforeseen issues that affect submission must be supported by approved Special Consideration.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Ongoing	Group project defined by students, guided by unit instructors. Implementation of ML in Python.	Self-directed learning (80 hr)
Week 01	Signals, Systems, and Models	Lecture (2 hr)
Week 01	Team formation and problem discovery	Tutorial (2 hr)
Week 02	Classical vs AI Models	Lecture (2 hr)
Week 02	Problem framing and proposal shaping	Tutorial (2 hr)
Week 03	Evolution of Modelling Approaches	Lecture (2 hr)
Week 03	Proposal finalisation and dataset commitment	Tutorial (2 hr)
Week 04	Machine Learning Foundations	Lecture (2 hr)
Week 04	Data understanding and preparation	Tutorial (2 hr)
Week 05	Evaluating and Visualising ML Performance	Lecture (2 hr)
Week 05	Baseline modelling and evaluation	Tutorial (2 hr)
Week 06	Common ML Models	Lecture (2 hr)
Week 06	Model optimisation and reflection	Tutorial (2 hr)
Week 07	Artificial Intelligence and Generative AI	Lecture (2 hr)
Week 07	Alternative model development, in-class quiz.	Tutorial (2 hr)
Week 08	AI in Engineering Practice	Lecture (2 hr)
Week 08	Model comparison and pipeline integration.	Tutorial (2 hr)
Week 09	Project Management Fundamentals	Lecture (2 hr)
Week 09	End-to-end system design	Tutorial (2 hr)
Week 10	Technical Report Writing	Lecture (2 hr)
Week 10	Technical reporting and argumentation	Tutorial (2 hr)
Week 11	Applying AI to Projects	Lecture (2 hr)
Week 11	Presentation and demonstration readiness, in-class quiz.	Tutorial (2 hr)
Week 12	From ENGG2112 to ENGG3112: shift from a technical focus to business and societal impact.	Lecture (2 hr)
Week 12	Reflection and learning synthesis	Tutorial (2 hr)
Week 13	Integration and Reflection	Lecture (2 hr)
Week 13	Final project presentation	Tutorial (2 hr)

Attendance and class requirements

The lecture is recorded.
Tutorials are compulsory in person.
Project work that requires out-of-class coordination with fellow teammates.
The final presentation must involve all group members and be presented.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

At the completion of this unit, you should be able to:

LO1. Articulate reasoning and justify creative solutions to an engineering problem solvable using machine learning.
LO2. Find and interpret information autonomously and demonstrate capacity for independent learning.
LO3. Apply basic project management techniques to manage self and others in a team, and to plan an engineering solution.
LO4. Analyze and manipulate medium-scale datasets to extract meaningful machine learning models.
LO5. Under guidance, identify and apply appropriate machine learning concepts and methods to develop an engineering solution.
LO6. Understand how data is stored, interpreted and processed for engineering applications.
LO7. Appreciate the context of data-driven engineering solutions, including applicable regulatory frameworks, standards, community expectations and commercialization opportunities.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Alignment with Competency standards

Outcomes	Competency standards
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.2. Information literacy and the ability to manage information and documentation. 4.5. An ability to undertake problem solving, design and project work within a broad contextual framework accommodating social, cultural, ethical, legal, political, economic and environmental responsibilities as well as within the principles of sustainable development and health and safety imperatives. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 3.2. Information literacy and the ability to manage information and documentation. 3.7. A capacity for lifelong learning and professional development and appropriate professional attitudes.
	Engineers Australia Curriculum Performance Indicators - 3.1. An ability to communicate with the engineering team and the community at large. 3.6. An ability to function as an individual and as a team leader and member in multi-disciplinary and multi-cultural teams. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 4.4. Skills in implementing and managing engineering projects within the bounds of time, budget, performance and quality assurance requirements. 4.5. An ability to undertake problem solving, design and project work within a broad contextual framework accommodating social, cultural, ethical, legal, political, economic and environmental responsibilities as well as within the principles of sustainable development and health and safety imperatives. 5.9. Skills in documenting results, analysing credibility of outcomes, critical reflection, developing robust conclusions, reporting outcomes.
	Engineers Australia Curriculum Performance Indicators - 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 4.2. Ability to use a systems approach to complex problems, and to design and operational performance. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 1.1. Developing underpinning capabilities in mathematics, physical, life and information sciences and engineering sciences, as appropriate to the designated field of practice. 1.2. Tackling technically challenging problems from first principles. 2.2. Application of enabling skills and knowledge to problem solution in these technical domains. 4.1. Advanced level skills in the structured solution of complex and often ill defined problems. 5.5. Skills in the development and application of mathematical, physical and conceptual models, understanding of applicability and shortcomings.
	Engineers Australia Curriculum Performance Indicators - 2.4. Advanced knowledge and capability development in one or more specialist areas through engagement with: (a) specific body of knowledge and emerging developments and (b) problems and situations of significant technical complexity. 3.2. Information literacy and the ability to manage information and documentation. 5.4. Skills in the selection and application of appropriate engineering resources tools and techniques, appreciation of accuracy and limitations;. 5.8. Skills in recognising unsuccessful outcomes, sources of error, diagnosis, fault-finding and re-engineering.
	Engineers Australia Curriculum Performance Indicators - 2.1. Appropriate range and depth of learning in the technical domains comprising the field of practice informed by national and international benchmarks. 2.3. Meaningful engagement with current technical and professional practices and issues in the designated field. 3.4. An understanding of and commitment to ethical and professional responsibilities.

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

More gudiance and examplars will be provided in class with futher discussions in lectures. Tutors will provide support to students based on their questions. modifcation of the two quzzes and updates to the final reflection piece with emptheis on employabilities and project debreif.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

ENGG2112: Multi-disciplinary Engineering

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Early feedback task

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Alignment with Competency standards

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links