- Code COMP4660
- Unit Value 6 units
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.
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 /
- 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
- Neural networks/Deep learning assignment 15%
- Neural networks/Deep learning/Fuzzy midterm quiz 15%
- Evolutionary/hybrid assignment 15%
- Active participation 5%
- Final Exam 50%
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WorkloadThirty hours of lectures and six two-hour tutorial/laboratory sessions
Requisite and Incompatibility
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- Student Contribution Band:
- Unit value:
- 6 units
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Class summaries, if available, can be accessed by clicking on the View link for the relevant class number.
|Class number||Class start date||Last day to enrol||Census date||Class end date||Mode Of Delivery||Class Summary|
|4042||19 Feb 2018||27 Feb 2018||31 Mar 2018||25 May 2018||In Person||N/A|