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Unit outline_

COMP9007: Algorithms

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

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

Academic unit Computer Science
Credit points 6
Prerequisites
? 
None
Corequisites
? 
None
Prohibitions
? 
COMP5211
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: LO1 LO3
Tutorial quiz Quizzes
10% Multiple weeks Three short quizzes during tutorials
Outcomes assessed: LO1 LO3
Small continuous assessment Assignments
30% Multiple weeks Three short assignments
Outcomes assessed: LO1 LO2 LO3 LO4

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 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.

Use of generative artificial intelligence (AI) and automated writing tools

You may only use generative AI and automated writing tools in assessment tasks if you are permitted to by your unit coordinator. If you do use these tools, you must acknowledge this in your work, either in a footnote or an acknowledgement section. The assessment instructions or unit outline will give guidance of the types of tools that are permitted and how the tools should be used.

Your final submitted work must be your own, original work. You must acknowledge any use of generative AI tools that have been used in the assessment, and any material that forms part of your submission must be appropriately referenced. For guidance on how to acknowledge the use of AI, please refer to the AI in Education Canvas site.

The unapproved use of these tools or unacknowledged use will be considered a breach of the Academic Integrity Policy and penalties may apply.

Studiosity is permitted unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission as detailed on the Learning Hub’s Canvas page.

Outside assessment tasks, generative AI tools may be used to support your learning. The AI in Education Canvas site contains a number of productive ways that students are using AI to improve their learning.

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 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 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 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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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.

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