COMPSCI 717 : Fundamentals of Algorithmics


2024 Semester One (1243) (30 POINTS)

Course Prescription

Fundamental techniques are covered for the design of algorithms such as greedy algorithms, divide-and-conquer, and dynamic programming. Data structures are explored that help implement algorithms. Essential tools are taught for analysing algorithms, for example worst- and average-case analyses of space and time. Recommended preparation: COMPSCI 120, 130

Course Overview

The course provides a comprehensive introduction to the design and analysis of algorithms at the postgraduate level. It prepares students, who have not had an introduction to this topic for advanced studies of most computer science subjects, such as artificial intelligence and data science. The ultimate aim of this course is to give students the skill to create computer code that can provably and efficiently solve every instance of a given problem. For this purpose, an in-depth understanding is required of why the algorithm works correctly and does the best job it can. Proof techniques, such as direct proofs, proofs by contraposition or contradiction, and induction principles are discussed. Algorithmic design paradigms such as greedy, divide-and-conquer, and dynamic programming are explained in general and with concrete examples. An elementary toolbox is established for analysing the worst-case and average-case resources in terms of time and space required by an algorithm. Data structures, such as records, lists, graphs, hash tables, priority queues, and trees, will be used to implement algorithms efficiently. An opportunity is provided to delve into deeper topics of algorithmics, including probabilistic, parallel, or approximate algorithms, or algorithmic complexity theory. The latter provides principled methods in showing the infeasibility of algorithmic solutions, or the likely intractability of computational problems.

Course Requirements

Restriction: COMPSCI 220, 320, SOFTENG 250, 284

Capabilities Developed in this Course

Capability 3: Knowledge and Practice
Capability 4: Critical Thinking
Capability 5: Solution Seeking
Capability 6: Communication
Graduate Profile: Master of Data Science

Learning Outcomes

By the end of this course, students will be able to:
  1. Explain three fundamental programming paradigms, that is, greedy algorithms, divide-and-conquer algorithms, and dynamic programming, and describe examples of algorithms from each class (Capability 3)
  2. Solve an elementary computational problem by modelling, designing, and implementing an efficient algorithm (Capability 4 and 5)
  3. Analyse algorithms with respect to their asymptotic worst-case behaviour in terms of time and space (Capability 3 and 4)
  4. Reason about the correctness of elementary algorithms (Capability 4)
  5. Distinguish between problems and their algorithmic solutions, and between efficient and inefficient solutions; relate these distinctions to the basic complexity classes of problems, namely P, NP, and NP-complete problems (Capability 3 and 4)
  6. Implement selected algorithms in the context of motivating applications using a standard programming language (Capability 3, 5 and 6)
  7. Communicate their work on an implementation of an algorithm and an algorithm analysis via a written or oral assignment. (Capability 3, 4 and 6)


Assessment Type Percentage Classification
Assignments 40% Individual Coursework
Test 20% Individual Test
Final Exam 40% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6 7
Final Exam


Tuākana Science is a multi-faceted programme for Māori and Pacific students providing topic specific tutorials, one-on-one sessions, test and exam preparation and more. Explore your options at

Workload Expectations

This course is a 30-point course and students are expected to spend 20 hours per week involved in a 30-point course they are enrolled in.

For this course, each week you can expect:

  • 4 hours of lectures
  • 10 hours of reading and thinking about the content
  • 6 hours of work on assignments and/or test preparation

Delivery Mode

Campus Experience

Attendance is encouraged at scheduled activities including lectures and tutorials. 
Lectures will be available as recordings. Other learning activities including tutorials may not be available as recordings.
The course may include live online events including office hours and group discussions.
Attendance on campus is required for the test and exam.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course materials are made available in a learning and collaboration tool called Canvas which also includes reading lists and lecture recordings (where available).

Please remember that the recording of any class on a personal device requires the permission of the instructor.

Recommended Readings:
  • G. Brassard and P. Bratley: Fundamentals of Algorithmics, Prentice-Hall, 1996. 
  • J. Kleinberg and E. Tardos: Algorithm Design, (Addison Wesley 2006) 
  • T.H. Cormen: Algorithms Unlocked (ebook) , 2013 
  • M. J. Dinneen, G. Gimel'farb and M. C. Wilson: Introduction to Algorithms and Data Structures (4th electronic edition, ebook), 2018  

Student Feedback

During the course Class Representatives in each class can take feedback to the staff responsible for the course and staff-student consultative committees.

At the end of the course students will be invited to give feedback on the course and teaching through a tool called SET or Qualtrics. The lecturers and course co-ordinators will consider all feedback.

Your feedback helps to improve the course and its delivery for all students.

Student feedback is encouraged at any time during the course.   We adapt for this bridging course due to different levels of student background.

Other Information

Need to know how to program in one of the following languages: Python, C++, Rust, Java, Go, C#

Academic Integrity

The University of Auckland will not tolerate cheating, or assisting others to cheat, and views cheating in coursework as a serious academic offence. The work that a student submits for grading must be the student's own work, reflecting their learning. Where work from other sources is used, it must be properly acknowledged and referenced. This requirement also applies to sources on the internet. A student's assessed work may be reviewed for potential plagiarism or other forms of academic misconduct, using computerised detection mechanisms.

Class Representatives

Class representatives are students tasked with representing student issues to departments, faculties, and the wider university. If you have a complaint about this course, please contact your class rep who will know how to raise it in the right channels. See your departmental noticeboard for contact details for your class reps.


The content and delivery of content in this course are protected by copyright. Material belonging to others may have been used in this course and copied by and solely for the educational purposes of the University under license.

You may copy the course content for the purposes of private study or research, but you may not upload onto any third party site, make a further copy or sell, alter or further reproduce or distribute any part of the course content to another person.

Inclusive Learning

All students are asked to discuss any impairment related requirements privately, face to face and/or in written form with the course coordinator, lecturer or tutor.

Student Disability Services also provides support for students with a wide range of impairments, both visible and invisible, to succeed and excel at the University. For more information and contact details, please visit the Student Disability Services’ website

Special Circumstances

If your ability to complete assessed coursework is affected by illness or other personal circumstances outside of your control, contact a member of teaching staff as soon as possible before the assessment is due.

If your personal circumstances significantly affect your performance, or preparation, for an exam or eligible written test, refer to the University’s aegrotat or compassionate consideration page

This should be done as soon as possible and no later than seven days after the affected test or exam date.

Learning Continuity

In the event of an unexpected disruption, we undertake to maintain the continuity and standard of teaching and learning in all your courses throughout the year. If there are unexpected disruptions the University has contingency plans to ensure that access to your course continues and course assessment continues to meet the principles of the University’s assessment policy. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator/director, and if disruption occurs you should refer to the university website for information about how to proceed.

The delivery mode may change depending on COVID restrictions. Any changes will be communicated through Canvas.

Student Charter and Responsibilities

The Student Charter assumes and acknowledges that students are active participants in the learning process and that they have responsibilities to the institution and the international community of scholars. The University expects that students will act at all times in a way that demonstrates respect for the rights of other students and staff so that the learning environment is both safe and productive. For further information visit Student Charter


Elements of this outline may be subject to change. The latest information about the course will be available for enrolled students in Canvas.

In this course students may be asked to submit coursework assessments digitally. The University reserves the right to conduct scheduled tests and examinations for this course online or through the use of computers or other electronic devices. Where tests or examinations are conducted online remote invigilation arrangements may be used. In exceptional circumstances changes to elements of this course may be necessary at short notice. Students enrolled in this course will be informed of any such changes and the reasons for them, as soon as possible, through Canvas.

Published on 31/10/2023 10:51 a.m.