ENGSCI 712 : Computational Algorithms for Signal Processing
Engineering
2024 Semester Two (1245) (15 POINTS)
Course Prescription
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. Applications covered by this module are predictive maintenance, activity recognition, and spectrometric analysis. A student who successfully completes this part of the course will: 1) Be able to Develop Algorithms for time-series classification and time-series exogeneous regression. 2) Be able to Apply Systematic time-series feature engineering. 3) Be able to interpret time-series features 4) Be able to design and model in Python using libraries like sklearn and tsfresh.
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. A student who successfully completes this part of the course will: 1) Be able to perform hand calculations and develop Algorithms for The Artificial Neuron model, The Multi-Layer Perceptron model, The Self-Organising Map and the Radial Basis Function Network in Matlab programming language and interpret the algorithm performance. 2) Be able to identify, understand and interpret the functionality of: the component parts of the brain, components parts of the neuron, typical brain patterns observed in the EEG and Epilepsy.
Capabilities Developed in this Course
| Capability 3: | Knowledge and Practice |
| Capability 4: | Critical Thinking |
| Capability 5: | Solution Seeking |
Learning Outcomes
- Understand and analyse Knowledge of basic neuroscience - namely, component parts of the brain, components parts of the neuron, signals that arise from neurons, the EEG and typical brain patterns observed, epilepsy and the EEG (Capability 3.1 and 4.1)
- Analyse and apply Analysis of how neurons in the brain are similar to artificial neural network models (Capability 3.1 and 4.1)
- Create Create and synthesis the 4 common ANN models taught in the course. (Namely, The Artificial Neuron model, The Multi-Layer Perceptron, The Self-Organising Map and the Radial Basis Function Network) (Capability 3.1, 3.2, 4.1, 4.2 and 5.1)
- Develop and demonstrate Be able to Develop Algorithms for time-series classification and time-series exogeneous regression signal processing applications (Capability 3.1, 3.2, 4.1, 4.2 and 5.1)
- Apply Be able to Apply Systematic time-series feature engineering and interpret time-series features (Capability 3.1, 3.2, 4.1, 4.2 and 5.1)
- Design, develop, evaluate, and validate Be able to design and model in Python using libraries like sklearn and tsfresh and model ANN in Matlab (Capability 3.1, 3.2, 4.1, 4.2 and 5.1)
Assessments
| Assessment Type | Percentage | Classification |
|---|---|---|
| Assignments | 30% | Individual Coursework |
| Test | 20% | Individual Test |
| Final Exam | 50% | Individual Examination |
| 3 types | 100% |
| Assessment Type | Learning Outcome Addressed | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 1 | 2 | 3 | 4 | 5 | 6 | |||||
| Assignments | ||||||||||
| Test | ||||||||||
| 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 each week in 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
Lectures will be available as recordings. Other learning activities including tutorials will not be available as recordings.
The course will include live online events including tutorials.
Attendance on campus is required for tests.
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.
Course notes and lecture recordings will be provided on Canvas.
Health & Safety
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.
Regular office hours will be provided.
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.
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 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.
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.
Disclaimer
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.