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Unit outline_

COMP5270: Randomised and Advanced Algorithms

Semester 2, 2024 [Normal day] - Camperdown/Darlington, Sydney

This unit of study will provide a rigorous introduction to a range of techniques and paradigms central to modern algorithm design, with a focus on randomised algorithms. The unit will emphasise the theoretical underpinnings of these algorithms and their mathematical guarantees, and provide intuition and understanding through a range of practical applications and examples such as probabilistic data structures, hashing, approximation algorithms, and streaming algorithms.

Unit details and rules

Academic unit Computer Science
Credit points 6
Prerequisites
? 
COMP9123 or COMP2123 or COMP2823
Corequisites
? 
None
Prohibitions
? 
COMP4270
Assumed knowledge
? 

COMP3027 or equivalent; Discrete Maths and Probability (MATH1064) or equivalent

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Clement Canonne, clement.canonne@sydney.edu.au
Lecturer(s) Clement Canonne, clement.canonne@sydney.edu.au
The census date for this unit availability is 2 September 2024
Type Description Weight Due Length
Supervised exam
? 
Final exam
Final exam, on paper.
40% Formal exam period 2 hours
Outcomes assessed: LO1 LO3 LO4 LO6 LO7
Participation Theoretical analysis
Choose and solve one of the suggested problems (list provided in syllabus).
6% Multiple weeks
Closing date: 01 Nov 2024
No time limit
Outcomes assessed: LO1 LO7 LO6 LO5 LO4 LO3 LO2
Assignment Assignment 1
Written assignment, with a programming component
15% Week 04
Due date: 30 Aug 2024 at 23:59
No time limit.
Outcomes assessed: LO1 LO3 LO4 LO5 LO6 LO7
Assignment Assignment 2
Written assignment, with a programming component
15% Week 07
Due date: 20 Sep 2024 at 23:59
No time limit.
Outcomes assessed: LO1 LO3 LO4 LO5 LO6 LO7
Assignment Assignment 3
Written assignment, with a programming component
15% Week 10
Due date: 11 Oct 2024 at 23:59
No time limit.
Outcomes assessed: LO1 LO3 LO4 LO5 LO6 LO7
Participation Weekly quiz
Weekly MCQ to assess your understanding of the lectures.
9% Weekly No time limit
Outcomes assessed: LO2 LO6 LO5

Assessment summary

Detailed information for each assessment can be found on the ed forum of the unit.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2021 (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.

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:

These penalties apply when written work is submitted after 11:59pm on the due date. Deduction of 5% of the maximum mark for each of the first 2 calendar days after the due date. Submissions made after the 2nd calendar day will receive 0 marks.

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.

Support for students

The Support for Students Policy 2023 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 2023. 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

WK Topic Learning activity Learning outcomes
Week 01 Basics of discrete probability, randomised algorithms, linearity of expectation; application to some specific algorithms Lecture (2 hr) LO1 LO2 LO3 LO4 LO5
Week 02 Concentration bounds, probability amplification, median trick Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6
Week 03 Coupon Collector, Load Balancing, Power of Two Choices Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 04 Derandomisation: Max-Cut, Method of Conditional Expectations Lecture (2 hr) LO1 LO4 LO5 LO6 LO7
Week 05 Graph algorithms: Randomised Min-Cut, MST in expected linear time Lecture (2 hr) LO3 LO4 LO5 LO6 LO7
Week 06 Probabilistic data structures I: Hashing and Bloom filters Lecture (2 hr) LO2 LO3 LO4 LO5 LO6 LO7
Week 07 Probabilistic data structures II: Johnson-Lindenstrauss, LSH Lecture (2 hr) LO2 LO3 LO4 LO6 LO7
Week 08 Streaming and Sketching I: definitions, examples, first algorithms Lecture (2 hr) LO1 LO2 LO3 LO4 LO5 LO6 LO7
Week 09 Streaming and Sketching II: CountSketch, Count–min Sketch Lecture (2 hr) LO2 LO3 LO4 LO6 LO7
Week 10 Linear Programming and Randomised Rounding Lecture (2 hr) LO2 LO3 LO4 LO5 LO6 LO7
Week 11 Randomised Embeddings: FRT algorithm, and applications Lecture (2 hr) LO3 LO4 LO5 LO6 LO7
Week 12 Special topic Lecture (2 hr) LO3 LO4 LO5 LO6 LO7
Week 13 Review Lecture (2 hr) LO1 LO3 LO4 LO5 LO6 LO7
Weekly Discussion and practice (exercise solving) related to the week's lecture. Tutorial (2 hr) LO1 LO3 LO4 LO5 LO6 LO7

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 required readings for this unit will be provided on the Ed forum of the unit.

Suggested (optional) readings (all available through the University of Sydney's library):

  • Mitzenmacher, M., & Upfal, E. (2005). Probability and computing : randomized algorithms and probabilistic analysis. Cambridge University Press.
  • Motwani, R., & Raghavan, P. (1996). Randomized algorithms. ACM Computing Surveys, 28(1), 33–37. https://doi.org/10.1145/234313.234327

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. Produce a clear account of an algorithm, that would allow others to understand and implement it
  • LO2. Learn about new algorithms by searching for descriptions in textbooks or online
  • LO3. Read, understand, analyze and modify a given algorithm. Ability to design efficient algorithmic solutions for given problems and evaluate the proposal
  • LO4. Analyze the complexity of a given algorithm, in terms of the different computational resources involved (time, memory, etc.)
  • LO5. Understand the fundamental concepts of randomisation and randomness in computing
  • LO6. Knowledge and mastery of key techniques to design and analyse randomised algorithms.
  • LO7. Apply knowledge of fundamental randomized algorithms and algorithmic techniques to several problems, especially streaming, optimization, scheduling, and graph problems.

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

Alignment with Competency standards

Outcomes Competency standards
LO1
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
LO2
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.2 (L1). Mathematical and computational methods. (Level 1- Contributing to required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
LO3
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
LO4
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
LO5
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
LO6
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
LO7
Stage 1 Competency Standard for Professional Engineer (UG) - EA
1.2 (L2). Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3). Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
Stage 1 Competency Standard for Professional Engineer (UG) -
Competency code Taught, Practiced or Assessed Competency standard
1.2 (L2) A P T Mathematical and computational methods. (Level 2- Attaining required standard (Bachelor Honours standard)) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
1.2 (L3) A P T Mathematical and computational methods. (Exceeding required standard) Conceptual understanding of the mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.

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

N/A (first time the unit is offered)

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