• Offered by School of Computing
  • ANU College ANU College of Engineering and Computer Science
  • Classification Advanced
  • Course subject Computer Science
  • Areas of interest Computer Science, Information Technology
  • Academic career PGRD
  • Mode of delivery In Person
  • Co-taught Course

This course has been adjusted for remote participation in Semester 1 2021.

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

Indicative Assessment

  1. Lecture and lab quizzes (15) [LO 1,2,3]
  2. Neural networks assignment (20) [LO 1,2,3]
  3. Advanced Assignment (20) [LO 1,2,3]
  4. 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

To enrol in this course you must have either completed COMP6670 OR (be enrolled in the Master of Applied Data Analytics and have completed 6 units of either COMP6710 or COMP6730 or COMP7230).

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
Note: Please note that fee information is for current year only.

Offerings, Dates and Class Summary Links

There are no current offerings for this course.

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