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Unit of study_

COMP2922: Models of Computation (Adv)

Semester 2, 2023 [Normal day] - Camperdown/Darlington, Sydney

This unit provides an introduction to the foundations of computing. The main aims are to introduce and compare different models of computation based on state-machines, grammars and algebra, and logic.

Unit details and rules

Unit code COMP2922
Academic unit Computer Science
Credit points 6
Distinction level result in (INFO1103 OR INFO1903 OR INFO1113)
Assumed knowledge

(MATH1004 OR MATH1904 OR MATH1064 OR MATH2069 OR MATH2969) AND (INFO1105 OR INFO1905 OR COMP2123 OR COMP2823)

Available to study abroad and exchange students


Teaching staff

Coordinator Sasha Rubin,
Type Description Weight Due Length
Supervised exam
hurdle task
Final exam
Final written examination.
50% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO4 LO5 LO6 LO11 LO12 LO13 LO14
Assignment A1
take home assignment
9% Week 05
Due date: 03 Sep 2023 at 23:59

Closing date: 08 Sep 2023
10 working days
Outcomes assessed: LO1 LO2 LO3 LO6
Assignment AA1
Take home assignment
0% Week 07
Due date: 17 Sep 2023 at 23:59

Closing date: 22 Sep 2023
10 working days
Outcomes assessed: LO1
Assignment A2
take home assignment
9% Week 08
Due date: 24 Sep 2023 at 23:59

Closing date: 29 Sep 2023
10 working days
Outcomes assessed: LO1 LO8 LO7 LO6 LO5
Assignment AA2
0% Week 09
Due date: 04 Oct 2023 at 23:59

Closing date: 09 Oct 2023
10 working days
Outcomes assessed: LO1
Assignment A3
take home assignment
9% Week 10
Due date: 15 Oct 2023 at 23:59

Closing date: 20 Oct 2023
10 working days
Outcomes assessed: LO1 LO11 LO6
Assignment AA3
Take home assignment
0% Week 11
Due date: 22 Oct 2023 at 23:59

Closing date: 27 Oct 2023
10 working days
Outcomes assessed: LO1
Assignment A4
take home assignment
9% Week 13
Due date: 05 Nov 2023 at 23:59

Closing date: 10 Nov 2023
10 working days
Outcomes assessed: LO1 LO15 LO14 LO13 LO12
Online task Quizzes
Weekly quizzes online
14% Weekly n/a
hurdle task = hurdle task ?

Assessment summary

Assignments count 36% of the final grade. How each assignment (4 from COMP2022 and 3 from COMP2922) contribute to this grade will be published on Ed in the first week.

Detailed information for each assessment can be found on Ed.


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


High distinction

85 - 100



75 - 84



65 - 74



50 - 64



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


For more information see guide to grades.

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:

To assist you in improving your assignment work, we will aim to provide feedback to you very quickly following each submission. For this reason, assignments submissions will not be accepted more than 5 days late. For late submissions of 5 days or less, if you do not have an approved simple extension or special consideration, the submission will incur the usual penalty in accordance with University policy (5% per day). Also, because we cannot accept assignment submissions more than 5 days late, when a special consideration allows for a longer delay, you will be given a mark adjustment by reweighting your other assignments.

Academic integrity

The Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 Introduction, inductive definitions, formal languages Lecture and tutorial (4 hr) LO1
Week 02 Regular languages Lecture and tutorial (4 hr) LO1 LO2 LO3
Week 03 Regular languages Lecture and tutorial (4 hr) LO1 LO2 LO3 LO6
Week 04 Context-free grammars Lecture and tutorial (4 hr) LO1 LO5 LO8
Week 05 Context-free grammars Lecture and tutorial (4 hr) LO1 LO5 LO6
Week 06 Turing Machines Lecture and tutorial (4 hr) LO1 LO7
Week 07 Turing Machines Lecture and tutorial (4 hr) LO1 LO4 LO10 LO11
Week 08 Computational Complexity Lecture and tutorial (4 hr) LO1 LO9
Week 09 Propositional Logic Lecture and tutorial (4 hr) LO1 LO12
Week 10 Propositional Logic Lecture and tutorial (4 hr) LO1 LO13 LO14
Week 11 Predicate Logic Lecture and tutorial (4 hr) LO1 LO12
Week 12 Propositional Logic Lecture and tutorial (4 hr) LO1 LO15 LO16
Week 13 Predicate Logic Lecture (2 hr) LO1 LO13 LO14 LO16

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. demonstrate a knowledge of discrete mathematics, mathematical theorems and proofs
  • LO2. Design a deterministic (resp. nondeterministic) finite state machine to accept a specified language.
  • LO3. Generate a regular expression to represent a specified language.
  • LO4. Explain why the halting problem has no algorithmic solution.
  • LO5. Design a context-free grammar to represent a specified language.
  • LO6. Convert among equivalently powerful notations for a language, including among DFAs, NFAs, and regular expressions.
  • LO7. Explain the Church-Turing thesis and its significance.
  • LO8. Determine a language’s place in the Chomsky hierarchy (e.g., regular, context-free, Turing recognisable)
  • LO9. Define the classes P and NP.
  • LO10. Provide examples of uncomputable functions.
  • LO11. Prove that a problem is uncomputable by reducing a classic known uncomputable problem to it.
  • LO12. Convert logical statements from informal language to propositional and predicate logic expressions.
  • LO13. Apply formal methods of symbolic propositional and predicate logic, such as calculating validity of formulae and computing normal forms.
  • LO14. Use the rules of inference to construct proofs in propositional and predicate logic.
  • LO15. Describe how symbolic logic can be used to model real-life situations or applications, e.g., arising in computing contexts such as software analysis (e.g., program correctness), database queries, or algorithms.
  • LO16. Describe the strengths and limitations of propositional and predicate logic.

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

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

Expanded lecture and tutorials on automata.

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, or the similarity report available in ED ( or Gradescope ( 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



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