• Class Number 5164
  • Term Code 3260
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Hanna Kurniawati
    • Dr Edward Kim
    • Dr Hanna Kurniawati
  • Class Dates
  • Class Start Date 25/07/2022
  • Class End Date 28/10/2022
  • Census Date 31/08/2022
  • Last Date to Enrol 01/08/2022
SELT Survey Results

This course provides basic approaches for designing and analysing algorithms and data structures. It will focus on two fundamental problems in computing: Sorting and searching. It will cover various data structures and algorithm design techniques for solving these two classes of problems, as well as basic performance measures and analysis techniques for solving such problems.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Display an advanced understanding of a variety of algorithms, including linear selection, minimum spanning trees, single source shortest path, Huffman coding, etc, with real-life applications and the resource requirements.
  2. Expertly apply the most effective algorithmic techniques including dynamic programming, greedy policy, and divide-and-conquer, to solve some practical problems.
  3. Define and analyse time and space complexities of algorithms.
  4. Demonstrate experience in the design and implementation of algorithms for practical problems, using languages like C, C++.

Examination Material or equipment

Cheating will NOT be tolerated. Cheating means claiming someone else's work as yours. A student in this course is expected to be able to explain and defend any submitted assessment item. The course convenor can conduct or initiate an additional interview about any submitted assessment item for any student, at any time during the course. If there is a significant discrepancy between the two forms of assessment, it will be automatically treated as a case of suspected academic misconduct.

Required Resources

The information provided in this page is high level and tentative information. For more details, please see the wattle page of this class. Whenever there's discrepancy between this page and the wattle page, the information in the wattle page is the correct one.

Remote students can take this class. However, to get the most from this class, in-person attendance is highly recommended.

Whether you are on campus or studying remotely, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Intro to the Problem (Sorting + Searching) Computational Model Asymptotic Analysis Delivered by Hanna
2 Asymptotic Analysis Delivered by Hanna
3 Recurrence Analysis Delivered by Hanna
4 Probabilistic Analysis Delivered by Hanna
5 More on Sorting Delivered by Hanna
6 Abstract Data Structure: Binary Search Trees, Heap, and AVL Trees, Red Black Trees Delivered by Hanna
7 Mid-term exam Delivered by Hanna + Edward
8 Abstract Data Structure: Hashing Delivered by Edward
9 Algorithm design technique: Dynamic Programming Delivered by Edward
10 Algorithm design technique: Dynamic Programming Delivered by Edward
11 Algorithm design technique: Greedy Delivered by Edward
12 Intro to Problem complexity Recap Delivered by Edward/Hanna

Tutorial Registration


Assessment Summary

Assessment task Value Due Date Learning Outcomes
Assignment 1 20 % 18/08/2022 3
Mid term exam 20 % 21/09/2022 1,3
Assignment 2 20 % 28/09/2022 2
Final exam 40 % * 1-4

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 20 %
Due Date: 18/08/2022
Learning Outcomes: 3

Assignment 1

Asymptotic analysis. Compare to COMP3600, there will be additional questions.

Assessment Task 2

Value: 20 %
Due Date: 21/09/2022
Learning Outcomes: 1,3

Mid term exam

Cover all materials from week 1 to week 6. Compare to COMP3600, there will be additional questions.

Assessment Task 3

Value: 20 %
Due Date: 28/09/2022
Learning Outcomes: 2

Assignment 2

Data structures + Algorithm design techniques. Compare to COMP3600, there will be additional questions.

Assessment Task 4

Value: 40 %
Learning Outcomes: 1-4

Final exam

All materials. Compare to COMP3600, there will be additional questions.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment via wattle

Hardcopy Submission

No hardcopy submission

Late Submission

We provide 13 hours grace period. This means, assessments can be submitted up to 13 hours after the due date with no penalty. However, after this grace period ends, late penalty is 100%

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Hanna Kurniawati

Research Interests

Focus on computational representations and methods to enable robust decision theory to become practical software tools, with applications in robotics and the assurance of autonomous systems. Such software tools will enable robots to design their own strategies, such as deciding what data to use, how to gather the data, and how to move, for accomplishing various tasks well, despite various modelling errors and types of uncertainty, and despite limited to no information about the system and its operating environment.

Dr Hanna Kurniawati

By Appointment
Dr Edward Kim

Research Interests

Dr Edward Kim

Dr Hanna Kurniawati

Research Interests

Dr Hanna Kurniawati

By Appointment

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