COMP9601: Semester 1, 2026

Units COMP9601 Semester 1 2026 [Normal evening]

Unit outline_

COMP9601: Computer and Network Organisation

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

Overview

This unit of study provides an introduction to computer organisation and network protocols. It covers a broad range of topics including computer hardware, software architecture (operating systems, compilers, etc), and principles of communication network protocols. It is designed to give students an understanding of how software programs operate and run inside the computer hardware, and therefore the knowledge how to use computers most effectively.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	COMP5213
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Albert Zomaya, albert.zomaya@sydney.edu.au
Lecturer(s)	Ashek Ahmmed, ashek.ahmmed@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
Written exam	Final Exam This is a closed book examination - specified materials permitted.	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Practical skill	Assignment 1 Individual assessment comprises 10% of the total mark	10%	Week 05	-	AI allowed
Outcomes assessed:
Practical skill	Assignment 2 Individual assessment comprises of 10% of the total mark	10%	Week 09	--	AI allowed
Outcomes assessed:
Practical skill	Assignment 3 Groupwork assessment comprises 20% of the total mark	20%	Week 12	-	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Assignment 1: 10%;

Assignment 2: 10%;

Assignment 3: 20%

At least 40% of the final exam mark must be received.

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 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:

Consistent penalty of 5% per day late. Assignments more than 10 days late get 0.

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
Week 01	Introduction	Lecture (2 hr)
Week 02	Data Representation in Computer Systems (Part 1)	Lecture (2 hr)
Week 02	Base conversion	Tutorial (1 hr)
Week 03	Data Representation in Computer Systems (Part 2)	Lecture (2 hr)
Week 03	ASCII characters and floating-point representation	Tutorial (1 hr)
Week 04	Boolean Algebra and Logic Gates	Lecture (2 hr)
Week 04	Truth tables and Boolean functions	Tutorial (1 hr)
Week 05	Processor Architecture (Part 1)	Lecture (2 hr)
Week 05	Memory addressing and basic Unix/Linux commands	Tutorial (1 hr)
Week 06	Processor Architecture (Part 2)	Lecture (2 hr)
Week 06	Javap command	Tutorial (1 hr)
Week 07	Instruction Set Architectures	Lecture (2 hr)
Week 07	Java virtual machine	Tutorial (1 hr)
Week 08	Memory Hierarchy	Lecture (2 hr)
Week 08	Cache memory	Tutorial (1 hr)
Week 09	Input/Output and Storage Systems	Lecture (2 hr)
Week 09	Amdahl's law and magnetic disk technology	Tutorial (1 hr)
Week 10	Internet and Protocols	Lecture (2 hr)
Week 10	Message format in network layers	Tutorial (1 hr)
Week 11	Internetworking	Lecture (2 hr)
Week 11	Network delays	Tutorial (1 hr)
Week 12	Web Cache and Performance Measurements	Lecture (2 hr)
Week 12	Performance measurement	Tutorial (1 hr)
Week 13	Review	Lecture (2 hr)
Week 13	Solution of Assignment 2	Tutorial (1 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. understand the architecture of computer, especially instruction set, memory hierarchy and processor architecture through the introduction of a simplified processor that can run Java bytecode
LO2. understand network architecture and protocols (especially application level protocols and TCP/IP) and making effective use of a network tool and programming interface
LO3. study technical concepts and details, and present research reports (e.g., cache replacement policies and their advantages and disadvantages)
LO4. understand different number representations (i.e., binary, hexadecimal and decimal) and make use of these in the assembly language level.

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.

Two changes are introduced. - A2 (individual) deadline was previously week 10 (it has been updated to week 9) - A3 (group) deadline was previously week 11 (it has been updated to week 12) A1 (individual) deadline is unchanged (still week 5) The rationale behind this change is that A2 and A3 deadlines were only 1 week apart, which created difficulties for students.

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

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

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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