• Class Number 7520
  • Term Code 3360
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Sabrina Caldwell
    • Dr Sabrina Caldwell
  • Class Dates
  • Class Start Date 24/07/2023
  • Class End Date 27/10/2023
  • Census Date 31/08/2023
  • Last Date to Enrol 31/07/2023
    • Yue Yao
    • Dr Zhenyue Qin
SELT Survey Results

A neural network is a computational paradigm based on insights from the brain, consisting of many simple processing elements together producing complex computations. Deep learning uses many neural network layers for advanced feature recognition and prediction.

Bio-inspired Computing is the combination of computational intelligence and collective intelligence. These computational methods are used to solve complex problems, and modeled after design principles encountered in natural / biological systems, and tend to be adaptive, reactive, and distributed. The goal of bio-inspired computing is to produce computational tools with enhanced robustness, scalability, flexibility and which can interface more effectively with humans.

This course introduces the fundamental topics in bio-inspired computing, and build proficiency in the application of various algorithms in real-world problems. The course will also cover applications focused particularly on highly sophisticated interaction with users.


Learning Outcomes

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

  1. Compare and select the most appropriate method from: neural, deep learning, fuzzy, evolutionary or hybrid method for any application / data set.
  2. Successfully apply that method and analyse the results.
  3. Demonstrate an advanced theoretical understanding of the differences between these major bio-inspired computing methods, including the advantages and disadvantages of each

Research-Led Teaching

Neural networks and deep learning are closely allied with human intelligence and human-computer interaction (HCI), and understanding this relationship is key to building and designing effective bio-inspired computing solutions. In this course we expect you to learn more about this relationship through lectures, and optionally (for redeemable marks) through participating in research experiments of your choice from among the many online and in-person experiments on-going through the semester.

Field Trips

not applicable

Additional Course Costs

not applicable

Examination Material or equipment

The final exam will be online, though whether this will be entirely online (invigilated) or in-person or dual delivery is still to be determined. Students will be allowed one A4 sheet of paper with notes on both sides for the final exam.

Required Resources

Required content is published on the Wattle course page.

We may provide a list of suggested (but not required) reading.

Staff Feedback

Students will be given feedback in the following forms in this course:
  • Written comments
  • Verbal comments
  • Feedback to the whole class, to groups, to individuals, focus groups

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). The feedback given in these surveys is anonymous and provides the Colleges, University Education Committee and Academic Board with opportunities to recognise excellent teaching, and opportunities for improvement. The Surveys and Evaluation website provides more information on student surveys at ANU and reports on the feedback provided on ANU courses.

Class Schedule

Week/Session Summary of Activities Assessment
1 Introduction to the course including what we'll cover, how it will work, and assessments; Setting the context: bio-inspired computing, machine learning and AI, Intro do Neural Networks
2 Neural Networks: backpropagation, RELU, Softmax, input preprocessing, hidden units Quiz, Lab
3 Neural Networks: image compression, finite training sets, Cascor and Casper Quiz, Lab
4 Neural Networks review; Assignment 1 discussion and academic writing skills Quiz, Lab
5 Deep Learning: Introduction, CNN, Sequence Learning Quiz, Lab
6 Deep Learning: Representation learning, Generative models, Reinforcement Learning Quiz, Lab
7 Evolutionary Algorithms: Introduction, Genetic Algorithms, GAs for Feature Selection Assignment 1 paper due
8 Evolutionary Algorithms: Genetic programming, limitations of EAs, review Quiz, Lab
9 Integration: Swarm Intelligence, Self-organising maps, Bacterial Memetic Quiz, Lab, and Peer reviews due
10 Ethics and Responsible AI Quiz, lab
11 Deep Learning applications Quiz, Catch-up lab
12 Review of course, Final exam hints and tips Catch-up lab, Assignment 2 paper due

Tutorial Registration

ANU uses MyTimetable to enable you to view the timetable for your enrolled courses, browse, then self-allocate to small teaching activities / tutorials so you can best plan your time.

Please note that tutorials with low enrollments may be cancelled. In that case you will need to register for another tutorial.

Assessment Summary

Assessment task Value Due Date Return of assessment Learning Outcomes
Lecture and Lab quizzes 15 % * * 1,2,3
Assignment 1 20 % 29/09/2023 17/10/2023 1,2,3
Assignment 2 20 % 29/10/2023 14/11/2023 1,2,3
Peer evaluation 10 % 06/10/2023 17/10/2023 1,2,3
Final Exam 35 % * * 1,2,3

* 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 Misconduct 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 ANU Online website Students may choose not to submit assessment items through Turnitin. In this instance you will be required to submit, alongside the assessment item itself, hard copies of all references included in the assessment item.

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: 15 %
Learning Outcomes: 1,2,3

Lecture and Lab quizzes

Quizzes will be released during most weeks of the semester; these quizzes will focus on the content currently being taught.

Assessment Task 2

Value: 20 %
Due Date: 29/09/2023
Return of Assessment: 17/10/2023
Learning Outcomes: 1,2,3

Assignment 1

Exercise your new knowledge of Neural Networks with this written assignment.

Assessment Task 3

Value: 20 %
Due Date: 29/10/2023
Return of Assessment: 14/11/2023
Learning Outcomes: 1,2,3

Assignment 2

Apply your new skills in an area chosen from Neural Networks, Deep Learning and Evolutionary Algorithms to prepare a 'conference paper' on real data we will provide to you.

Assessment Task 4

Value: 10 %
Due Date: 06/10/2023
Return of Assessment: 17/10/2023
Learning Outcomes: 1,2,3

Peer evaluation

You will participate in a 'çonference paper' style review of Assignment 1 papers.

Assessment Task 5

Value: 35 %
Learning Outcomes: 1,2,3

Final Exam

The final exam is a summative exam designed to test your knowledge of what you have learned in the course. Further details on the final exam will be provided closer to the end of semester.

Academic Integrity

Academic integrity is a core part of our culture as a community of scholars. At its heart, academic integrity is about behaving ethically. This means that all members of the community commit to honest and responsible scholarly practice and to upholding these values with respect and fairness. The Australian National University commits to embedding the values of academic integrity in our teaching and learning. We ensure that all members of our community understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. The ANU expects staff and students to uphold high standards of academic integrity and act ethically and honestly, to ensure the quality and value of the qualification that you will graduate with. The University has policies and procedures in place to promote academic integrity and manage academic misconduct. Visit the following Academic honesty & plagiarism website for more information about academic integrity and what the ANU considers academic misconduct. The ANU offers a number of services to assist students with their assignments, examinations, and other learning activities. The Academic Skills and Learning Centre offers a number of workshops and seminars that you may find useful for your studies.

Online Submission

The ANU uses 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. While the use of Turnitin is not mandatory, the ANU highly recommends Turnitin is used by both teaching staff and students. For additional information regarding Turnitin please visit the ANU Online website.

Hardcopy Submission

For some forms of assessment (hand written assignments, art works, laboratory notes, etc.) hard copy submission is appropriate when approved by the Associate Dean (Education). Hard copy submissions must utilise the Assignment Cover Sheet. Please keep a copy of tasks completed for your records.

Late Submission

Late submission of assessment tasks without an extension are penalised at the rate of 5% of the possible marks available per working day or part thereof. Late submission of assessment tasks is not accepted after 10 working days after the due date, or on or after the date specified in the course outline for the return of the assessment item. Late submission is not accepted for take-home examinations.

Referencing Requirements

Accepted academic practice for referencing sources that you use in presentations can be found via the links on the Wattle site, under the file named “ANU and College Policies, Program Information, Student Support Services and Assessment”. Alternatively, you can seek help through the Students Learning Development website.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure The Course Convener may grant extensions 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.

Resubmission of Assignments

There is not enough time in the semester for resubmitting the assignment papers; no resubmissions will be accepted.

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 Sabrina Caldwell

Research Interests

Image and knowledge credibility, human-centred computing, human-computer interaction, web development and design, software engineering, neural networks and deep learning

Dr Sabrina Caldwell

By Appointment
By Appointment
Dr Sabrina Caldwell

Research Interests

Dr Sabrina Caldwell

By Appointment
By Appointment
Yue Yao

Research Interests

Yue Yao

By Appointment
Dr Zhenyue Qin

Research Interests

Image and knowledge credibility, human-centred computing, human-computer interaction, web development and design, software engineering, neural networks and deep learning

Dr Zhenyue Qin

By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions