COMP9007: Semester 1, 2020

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Units COMP9007 Semester 1 2020 [Normal evening]

Unit of study_

COMP9007: Algorithms

Semester 1, 2020 [Normal evening] - Camperdown/Darlington, Sydney

Overview

The study of algorithms is a fundamental aspect of computing. This unit of study covers data structures, algorithms, and gives an overview of the main ways of computational thinking from simple list manipulation and data format conversion, up to shortest paths and cycle detection in graphs. Students will gain essential knowledge in computer science, including basic concepts in data structures, algorithms, and intractability, using paradigms such as dynamic programming, divide and conquer, greed, local search, and randomisation, as well NP-hardness.

Unit details and rules

Unit code	COMP9007
Academic unit	Computer Science
Credit points	6
Prohibitions ?	COMP5211
Prerequisites ?	None
Corequisites ?	None
Assumed knowledge ?	This unit of study assumes that students have general knowledge of mathematics (especially Discrete Math) and problem solving. Having moderate knowledge about Data structures can also help students to better understand the concepts of Algorithms taught in this course.
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Mohammad Polash, masbaul.polash@sydney.edu.au

Type	Description	Weight	Due	Length
Final exam	Final Exam	60%	Formal exam period	2 hours
Outcomes assessed:
Tutorial quiz	Quizzes	10%	Multiple weeks	Three short quizzes during tutorials
Outcomes assessed:
Small continuous assessment	Assignments	30%	Multiple weeks	Three short assignments
Outcomes assessed:

Assessment summary

Assignment: There will be three assignments in total, due in Week 4, 8, and 12 respectively. These assignments will focus on the design and analysis of algorithms.

Quiz: There will be three short quizzes in total, during tutorial times in Week 4, 8, and 12 respectively, on canvas.

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.

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:

Late penalty for any assessment is 20% per day: e.g. if your work would have scored 60% and is 1 hour late then you get 40%, if your work would have scored 70% and is 28 hours late then you get 30%.

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	1. Unit introduction; 2. Algorithms and complexity; 3. Motivation and course outline; 4. Stable matching	Lecture and tutorial (3 hr)
Week 02	1. Algorithms design and analysis; 2. Asymptotic growth	Lecture and tutorial (3 hr)
Week 03	1. Data structures; 2. Recursive algorithms	Lecture and tutorial (3 hr)
Week 04	Graph Algorithms: BFS and DFS	Online class (3 hr)
Week 05	Greedy algorithms: Interval scheduling, Kruskal's algorithm, Dijkstra's algorithm	Online class (3 hr)
Week 06	Divide and conquer: Recurrences, sorting, integer multiplication, selection	Online class (3 hr)
Week 07	Dynamic programming 1	Online class (3 hr)
Week 08	Dynamic programming 2	Online class (3 hr)
Week 09	Network flows 1	Online class (3 hr)
Week 10	Network flows 2	Online class (3 hr)
Week 11	Advanced topic e.g. NP-hardness	Online class (3 hr)
Week 12	Advanced topic continued and reviewed	Online class (3 hr)
Week 13	Review and outlook	Online class (3 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.

Required readings

All readings for this unit can be accessed through the Library eReserve, available on Canvas.

Jon Kleinberg and Eva Tardos – Algorithm Design. Addison Wesley, 2006. 978-032129535-8

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 knowledge of fundamental algorithms for several problems, including graphs, greedy algorithms, divide-and-conquer, dynamic programming, and network flow as well as basic concepts of NP-completeness
LO2. collaborate in lectures/tutorials and exchange of ideas to solve algorithmic problems
LO3. understand and analyze given algorithms as well as ability to design algorithmic solutions for given problems
LO4. practice your writing presentation skills.

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.

No changes have been made since this unit was last offered

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

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

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