Unit outline_

COMP2922: Models of Computation (Adv)

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

This unit provides an introduction to the foundations of computing. Its main aim is to introduce and compare different models of computation.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
(INFO1110 or INFO1910 or INFO1113 or ENGG1810) and (Distinction level results in�INFO1110 or INFO1910 or INFO1113 or ENGG1810�or MATH1064 or MATH1964)
Corequisites
? 
COMP2123 or COMP2823
Prohibitions
? 
COMP2022
Assumed knowledge
? 

Discrete mathematics (e.g. MATH1064 or equivalent)

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Sasha Rubin, sasha.rubin@sydney.edu.au
The census date for this unit availability is 31 August 2026
Type Description Weight Due Length Use of AI
Written exam hurdle task Final exam
Final written examination.
60% Formal exam period 2 hours AI prohibited
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
In-class quiz Weekly in-tutorial test
In person, during tutorial, written
10% Multiple weeks 15 minutes each AI allowed
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
Out-of-class quiz Weekly Gradescope Quiz
Administered on Gradescope
10% Multiple weeks 7 days AI allowed
Outcomes assessed: LO1 LO2 LO3 LO4 LO5
Out-of-class quiz Early Feedback Task Early Feedback Task
Administered on Gradescope
0% Week 02
Due date: 16 Aug 2026 at 23:59

Closing date: 16 Aug 2026
7 days AI allowed
Outcomes assessed: LO5
Written test Mid-semester test
Mid-semester test (written, secured)
20% Week 08 1 hour AI prohibited
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO7
hurdle task = hurdle task ?
early feedback task = 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: weekly, administered on Gradescope.

In-tutorial tests: written, during weekly tutorials.

In-semester test: written, during week 8.

Final exam: written, during formal examination period.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name

Mark range

Description

High distinction

85 - 100

 

Distinction

75 - 84

 

Credit

65 - 74

 

Pass

50 - 64

 

Fail

0 - 49

When you don’t meet the learning outcomes of the unit to a satisfactory standard. It is a requirement of the School of Computer Science that in order to pass this unit, a student must achieve at least 40% in the written examination. For subjects without a final exam, the 40% minimum requirement applies to the corresponding major assessment component specified by the lecturer. A student must also achieve an overall final mark of 50 or more. Any student not meeting these requirements may be given a maximum final mark of no more than 45 regardless of their average.

 

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:

No late submissions are accepted for weekly quizzes and weekly in-tutorial tests. Approved SC for the weekly quizzes and in-tutorial tests will result in a mark adjustment. There are no simple extensions and no retakes. Approved SC for the in-semester test will result in a replacement test.

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.

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

WK Topic Learning activity Learning outcomes
Week 01 1. Main themes of the unit 2. Introduction to regular expressions Lecture (2 hr) LO1 LO4
1. Main themes of the unit 2. Introduction to regular expressions Tutorial (2 hr) LO1 LO4 LO7
Week 02 Regular languages Lecture (2 hr) LO1 LO2 LO3 LO5 LO7
Regular languages Tutorial (2 hr) LO1 LO2 LO3 LO5 LO7
Week 03 Regular languages Lecture (2 hr) LO1 LO2 LO3 LO4 LO7
Regular languages Tutorial (2 hr) LO1 LO2 LO3 LO4 LO7
Week 04 Regular languages Lecture (2 hr) LO1 LO2 LO3 LO5 LO7
Regular languages Tutorial (2 hr) LO1 LO2 LO3 LO5 LO7
Week 05 Regular languages Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Regular languages Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 06 Context-free grammars Lecture (2 hr) LO1 LO2 LO3 LO5 LO7
Context-free grammars Tutorial (2 hr) LO1 LO2 LO3 LO5 LO7
Week 07 Context-free grammars Lecture (2 hr) LO1 LO2 LO3 LO5 LO7
Context-free grammars Tutorial (2 hr) LO1 LO2 LO3 LO5 LO7
Week 08 Context-free grammars Lecture (2 hr) LO1 LO2 LO3 LO5 LO7
Context-free grammars Tutorial (2 hr) LO1 LO2 LO3 LO5 LO7
Week 09 Turing machines Lecture (2 hr) LO1 LO2 LO3 LO5 LO7
Turing machines Tutorial (2 hr) LO1 LO2 LO3 LO5 LO7
Week 10 Turing machines Lecture (2 hr) LO1 LO2 LO3 LO5 LO7
Turing machines Tutorial (2 hr) LO1 LO2 LO3 LO5 LO7
Week 11 Turing machines Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Turing machines Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 12 Turing Machines Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Turing machines Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 13 Industry talks and/or recap Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Industry talks and/or recap Tutorial (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7

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 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. Design abstract computational models (such as regular expressions, finite-state automata, context-free grammars, and Turing machines) for specified computational problems.
  • LO2. Describe the set of strings accepted/generated by a specified model (such as a specified regular expression, finite-state automaton, context-free grammar, or Turing machine).
  • LO3. Analyse abstract computational models, including converting among expressively equivalent models (e.g., among DFAs, NFAs, and regular expressions), as well as between different models (e.g., convert a regular expression to a context-free grammar).
  • LO4. Demonstrate a knowledge of examples of problems that cannot be solved by a given computational model.
  • LO5. Demonstrate a knowledge of basic discrete mathematics, including theorems, and proofs.
  • LO6. Characterise the computational resources needed to recognise a language, e.g., prove that a language is not regular, prove that a language is undecidable.
  • LO7. Construct and communicate mathematically sound proofs pertaining to abstract models of computation.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

Focused the advanced portion of this unit on problem solving and proofs; Lowered the workload.

Assessments for COMP2922 will include all those of COMP2022, as well as some additional ones. See Ed for details.

IMPORTANT: School guidelines relating to Academic Dishonesty and Plagiarism.

In assessing a piece of submitted work, the School of Computer Science may reproduce it entirely, may provide a copy to another member of faculty, and/or to an external plagiarism checking service or in-house computer program and may also maintain a copy of the assignment for future checking purposes and/or allow an external service to do so.

Computer programming assignments may be checked by specialist code similarity detection software. The Faculty of Engineering currently uses the MOSS similarity detection engine (see http://theory.stanford.edu/~aiken/moss/), or the similarity report available in ED (edstem.org) or Gradescope (gradescope.com). These programs work in a similar way to TurnItIn in that they check for similarity against a database of previously submitted assignments and code available on the internet, but they have added functionality to detect cases of similarity of holistic code structure in cases such as global search and replace of variable names, reordering of lines, changing of comment lines, and the use of white space.

All written assignments submitted in this unit of study will be submitted to the similarity detecting software program known as Turnitin. Turnitin searches for matches between text in your written assessment task and text sourced from the Internet, published works and assignments that have previously been submitted to Turnitin for analysis.There will always be some degree of text-matching when using Turnitin. Text-matching may occur in use of direct quotations, technical terms and phrases, or the listing of bibliographic material. This does not mean you will automatically be accused of academic dishonesty or plagiarism, although Turnitin reports may be used as evidence in academic dishonesty and plagiarism decision-making processes."

Additional costs

.

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.