ENGSCI 712 : Computational Algorithms for Signal Processing


2021 Semester Two (1215) (15 POINTS)

Course Prescription

Advanced topics in mathematical modelling and computational techniques, including topics on singular value decomposition, Principle Component Analysis and Independent Component Analysis, eigen-problems, and signal processing (topics on neural network models such as the multi-layer perception and self organising map).

Course Overview

The first part of the course is on Feature Extraction and Machine Learning. The second part of the course is on basic neuroscience and how bioinspired algorithms from the brain have given rise to the field of Artificial Neural Networks (ANNs). 

The Feature Extraction : combines classical theory on signal processing with modern machine learning (ML) Libraries for systematic feature engineering and analytics. Taught by Dr. Andreas Kempa-Liehr.

Artificial Neural Networks : Knowledge of basic neuroscience - namely, component parts of the brain, components parts of the neuron, typical brain patterns observed in the EEG and Epilepsy. Analysis application and synthesis of the 4 common ANN models will be taught in the course. These are: The Artificial Neuron model, The Multi-Layer Perceptron model, The Self-Organising Map and the Radial Basis Function Network. Taught by Associate Professor Charles Unsworth (Course Director)

Course Requirements

Prerequisite: 15 points from ENGSCI 311, 313, 314

Capabilities Developed in this Course

Capability 1: Disciplinary Knowledge and Practice
Capability 2: Critical Thinking
Capability 3: Solution Seeking
Capability 4: Communication and Engagement
Capability 5: Independence and Integrity

Learning Outcomes

By the end of this course, students will be able to:
  1. Analyse signal processing applications (Capability 1, 2, 3, 4 and 5)
  2. Apply signal processing algorithms, artificial neural network and machine learning models (Capability 1, 2 and 3)
  3. Create signal processing and artificial neural network models (Capability 3 and 5)
  4. Understand artificial neural networks and machine learning pipelines for signal processing applications (Capability 1, 2 and 4)
  5. Communicate data science processes for signal processing applications (Capability 4 and 5)
  6. Use Matlab and modern (Python based) machine learning libraries (Capability 1 and 3)


Assessment Type Percentage Classification
Assignments 20% Individual Coursework
Test 20% Individual Test
Final Exam 60% Individual Examination
Assessment Type Learning Outcome Addressed
1 2 3 4 5 6
Final Exam

Workload Expectations

This course is a standard 15 point course and students are expected to spend 10 hours per week involved in each 15 point course that they are enrolled in.

For this course, you can expect up to 2 hours of lectures, a 1 hour tutorial, 2 hours of reading and thinking about the content and 5 hours of work on assignments and/or test preparation.

Delivery Mode

Campus Experience

Attendance is required at scheduled activities including tests to complete and receive credit for components of the course.
Lectures will be available as recordings. Other learning activities including tutorials/labs will not be available as recordings.
The course will not include live online events including group discussions/tutorials.
The activities for the course are scheduled as a standard weekly timetable.

Learning Resources

Course notes will be provided both in paper format and as pdfs on Canvas.

Health & Safety

Students are expected to adhere to the guidelines outlined in the Health and Safety section of the Engineering Undergraduate Handbook.

Student Feedback

At the end of every semester 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 and respond with summaries and actions.

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

Class Representatives in each class can take feedback to the department and faculty staff-student consultative committees.

Digital 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.

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 against online source material 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.

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 http://disability.auckland.ac.nz

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 https://www.auckland.ac.nz/en/students/academic-information/exams-and-final-results/during-exams/aegrotat-and-compassionate-consideration.html.

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 your assessment is fair, and not compromised. Some adjustments may need to be made in emergencies. You will be kept fully informed by your course co-ordinator, and if disruption occurs you should refer to the University Website for information about how to proceed.

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 https://www.auckland.ac.nz/en/students/forms-policies-and-guidelines/student-policies-and-guidelines/student-charter.html.


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 you may be asked to submit your 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. The final decision on the completion mode for a test or examination, and remote invigilation arrangements where applicable, will be advised to students at least 10 days prior to the scheduled date of the assessment, or in the case of an examination when the examination timetable is published.

Published on 14/07/2021 09:20 p.m.