COMP2922: Semester 2, 2020

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Units COMP2922 Semester 2 2020 [Normal day]

Unit of study_

COMP2922: Models of Computation (Adv)

Semester 2, 2020 [Normal day] - Camperdown/Darlington, Sydney

Overview

This unit provides an introduction to the foundations of computational models, and their connection to programming languages/tools. The unit covers various abstract models for computation including Lambda Calculus, and Logic calculi (e.g. concept of formal proofs in propositional, predicate, and temporal logic). For each abstract model, we introduce programming languages/tools that are built on the introduced abstract computational models. We will discuss functional languages including Scheme/Haskell, and Prolog/Datalog.

Unit details and rules

Unit code	COMP2922
Academic unit	Computer Science
Credit points	6
Prohibitions ?	COMP2022
Prerequisites ?	Distinction level result in INFO1103 OR INFO1903 OR INFO1113
Corequisites ?	None
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	No

Teaching staff

Coordinator	Sasha Rubin, sasha.rubin@sydney.edu.au

Type	Description	Weight	Due	Length
Online task	Online Quiz Short weekly quizzes on Canvas	10%	-	n/a
Outcomes assessed:
Final exam (Take-home short release)	Final exam Final written examination.	50%	Formal exam period	3 hours
Outcomes assessed:
Assignment	Assignment A mix of theory and programming	40%	Multiple weeks	n/a
Outcomes assessed:
= hurdle task ? = Type D final exam ?

Assessment summary

Detailed information for each assessment can be found on Canvas.

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.

For more information see sydney.edu.au/students/guide-to-grades.

It is a policy 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.

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:

These penalties apply when written work is submitted after 11:59pm on the due date. Deduction of 20% of the maximum mark for each calendar day after the due date.

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.

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.

Weekly schedule

WK	Topic	Learning activity
Week 01	Propositional Logic	Lecture and tutorial (4 hr)
Week 02	Propositional Logic	Lecture and tutorial (4 hr)
Week 03	Predicate logic	Lecture and tutorial (4 hr)
Week 04	Predicate logic	Lecture and tutorial (4 hr)
Week 05	Regular languages, their representations, and their uses	Lecture and tutorial (4 hr)
Week 06	Regular languages, their representations, and their uses	Lecture and tutorial (4 hr)
Week 07	Regular languages, their representations, and their uses	Lecture and tutorial (4 hr)
Week 08	Context-free languages, their representations, and their uses	Lecture and tutorial (4 hr)
Week 09	Context-free languages, their representations, and their uses	Lecture and tutorial (4 hr)
Week 10	Universal models of computations, and their uses	Lecture and tutorial (4 hr)
Week 11	Universal models of computations, and their uses	Lecture and tutorial (4 hr)
Week 12	Revision of unit of study	Lecture and tutorial (4 hr)

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.

Outcomes
Graduate qualities

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

LO1. demonstrate a knowledge of discrete mathematics, mathematical theorems and proofs
LO2. hold an understanding of propositional logic, including syntax, semantics, proof systems, and some basic meta-theorems
LO3. demonstrate an ability to use languages/tools for propositional logic
LO4. hold an understanding of predicate logic, including syntax, semantics, proof systems, and some basic meta-theorems
LO5. hold an understanding of the concept of propositional and first order logic as a model of facts and of reasoning
LO6. hold an understanding of the concept of a formal language as a set of strings, and of operations on formal languages, in particular union, concatenation, and Kleene closure
LO7. demonstrate an ability to use regular languages and their representations by DFAs, NFAs, regular expressions, and regular grammars
LO8. demonstrate an ability to use context-free grammars as a model of formal languages
LO9. demonstrate an awareness of the Chomsky hierarchy, and the notions of decidability and intractability
LO10. demonstrate an awareness of universal models of computation, e.g., Turing machines, the lambda calculus and its application to functional programming

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

Responding to student feedback

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

Aligned some of the course material with textbooks.

Additional information

Reference texts:

Introduction to the theory of computation, by Michael Sipser.
Elements of the Theory of Computation, by Harry Lewis and Christos Papadimitriou.
Logic for Computer Scientists, by Uwe Schöning.

IMPORTANT: School policy 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.

Additional costs

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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

Assessment summary

Assessment criteria

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Additional costs

Disclaimer

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