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We are aiming for an incremental return to campus in accordance with guidelines provided by NSW Health and the Australian Government. Until this time, learning activities and assessments will be planned and scheduled for online delivery where possible, and unit-specific details about face-to-face teaching will be provided on Canvas as the opportunities for face-to-face learning become clear.

Unit of study_

COMP9007: Algorithms

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.

Details

Academic unit Computer Science
Unit code COMP9007
Unit name Algorithms
Session, year
? 
Semester 2, 2020
Attendance mode Normal evening
Location Camperdown/Darlington, Sydney
Credit points 6

Enrolment rules

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 and contact details

Coordinator Andre van Renssen, andre.vanrenssen@sydney.edu.au
Type Description Weight Due Length
Final exam (Open book) Type C final exam Final Exam
2 hour exam
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO3
Small continuous assessment Assignments
30% Multiple weeks Four assignments
Outcomes assessed: LO1 LO2 LO3 LO4
Tutorial quiz Quizzes
10% Weekly Ten short quizzes
Outcomes assessed: LO1 LO3
Type C final exam = Type C final exam ?
  • Assignments: There will be four assignments in total, due every three weeks, worth 30% of the final mark. These assignments will focus on the design and analysis of algorithms.
  • Quizzes: There will be 10 weekly quizzes in total, worth 10% of the final mark. 

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.

 

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 sydney.edu.au/students/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%.

Special consideration

If you experience short-term circumstances beyond your control, such as illness, injury or misadventure or if you have essential commitments which impact your preparation or performance in an assessment, you may be eligible for special consideration or special arrangements.

Academic integrity

The Current Student website provides information on academic honesty, academic dishonesty, 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 dishonesty or plagiarism seriously.

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

WK Topic Learning activity Learning outcomes
Week 01 Unit introduction; Stable matching Online class (3 hr) LO2
Week 02 Algorithms design and analysis; Asymptotic growth Online class (3 hr) LO2 LO3
Week 03 Data structures 1 Online class (3 hr) LO2 LO3 LO4
Week 04 Data structures 2 Online class (3 hr) LO2 LO3 LO4
Week 05 Graph Algorithms: BFS and DFS Online class (3 hr) LO1 LO2 LO3 LO4
Week 06 Greedy algorithms: Interval scheduling, Kruskal's algorithm, Dijkstra's algorithm Online class (3 hr) LO1 LO2 LO3 LO4
Week 07 Divide and conquer: Recurrences, sorting, integer multiplication, selection Online class (3 hr) LO1 LO2 LO3 LO4
Week 08 Dynamic programming 1 Online class (3 hr) LO1 LO2 LO3 LO4
Week 09 Dynamic programming 2 Online class (3 hr) LO1 LO2 LO3 LO4
Week 10 Network flows 1 Online class (3 hr) LO1 LO2 LO3 LO4
Week 11 Network flows 2 Online class (3 hr) LO1 LO2 LO3 LO4
Week 12 Review and outlook Online class (3 hr) LO2 LO4

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.

Prescribed 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 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 knowledge of fundamental algorithms for several problems, including graphs, greedy algorithms, divide-and-conquer, dynamic programming, and network flow
  • 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9
No changes have been made since this unit was last offered.

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.

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.

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.