This course has been adjusted for remote participation in Semester 1, 2022.
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:
- Compare and select the most appropriate method from: neural, deep learning, fuzzy, evolutionary or hybrid method for any application / data set.
- Successfully apply that method and analyse the results.
- Demonstrate an advanced theoretical understanding of the differences between these major bio-inspired computing methods, including the advantages and disadvantages of each.
Other Information
Note: Non-DADAN/MADAN students wanting to enrol in this course are required to seek approval from the MADA Program Convener.
Indicative Assessment
- Lecture and lab quizzes (15) [LO 1,2,3]
- Neural networks assignment (20) [LO 1,2,3]
- Advanced Assignment (20) [LO 1,2,3]
- Final exam (45) [LO 1,2,3]
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.
Workload
Thirty hours of lectures and six two-hour tutorial/laboratory sessions.
Inherent Requirements
Inherent requirements for this course are currently not available.
Requisite and Incompatibility
Prescribed Texts
None
Specialisations
Fees
Tuition fees are for the academic year indicated at the top of the page.
Commonwealth Support (CSP) Students
If you have been offered a Commonwealth supported place, your fees are set by the Australian Government for each course. At ANU 1 EFTSL is 48 units (normally 8 x 6-unit courses). More information about your student contribution amount for each course at Fees.
- Student Contribution Band:
- 2
- Unit value:
- 6 units
If you are a domestic graduate coursework student with a Domestic Tuition Fee (DTF) place or international student you will be required to pay course tuition fees (see below). Course tuition fees are indexed annually. Further information for domestic and international students about tuition and other fees can be found at Fees.
Where there is a unit range displayed for this course, not all unit options below may be available.
| Units | EFTSL |
|---|---|
| 6.00 | 0.12500 |
Course fees
- Domestic fee paying students
| Year | Fee |
|---|---|
| 2022 | $4740 |
- International fee paying students
| Year | Fee |
|---|---|
| 2022 | $6000 |
Offerings, Dates and Class Summary Links
ANU utilises MyTimetable to enable students to view the timetable for their enrolled courses, browse, then self-allocate to small teaching activities / tutorials so they can better plan their time. Find out more on the Timetable webpage.
Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
Autumn Session
| Class number | Class start date | Last day to enrol | Census date | Class end date | Mode Of Delivery | Class Summary |
|---|---|---|---|---|---|---|
| Intensive Course | ||||||
| 3572 | 14 Mar 2022 | 14 Mar 2022 | 01 Apr 2022 | 13 May 2022 | In Person | N/A |
