This unit looks at the development and application of biologically inspired models of computation. We study: basic components of a natural neural systems: synapses, dendrites and neurons and their computational models; fundamental concepts of data and signal encoding and processing; neural network architectures: pattern association networks, auto associative networks, feedforward networks, competitive networks, self organizing networks and recurrent networks; plasticity and learning. Hebb rule, supervised learning, reinforced learning, error-correcting learning, unsupervised learning, competitive learning, self-organization.
2 hrs lectures/wk, 2 hrs laboratories/wk
Two-hour lecture and two-hour tutorial (or laboratory) (requiring advance preparation) a minimum of 2-3 hours of personal study per one hour of contact time in order to satisfy the reading and assignment expectations. You will need to allocate up to 5 hours per week in some weeks, for use of a computer, including time for newsgroups/discussion groups.
Contact hours: Friday 12-2pm
Minh Viet Le
Contact hours: Monday 5-6pm
At the completion of this unit students will:
Examination (3 hours): 60%; In-semester assessment: 40%
|Assessment Task||Value||Due Date|
|Unit Test||20%||Week 8 lecture|
|Applications of Neural Network Algorithms||20%||Stage 1 due Week 9 (hurdle), Stage 2 due start Week 11 lecture|
|Examination 1||60 %||To be advised|
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You will need access to a Neural Network tool such as:
All the above softwares are available in the 24 hour labs B3.45, B3.46, B3.46b at the Caulfield Campus. Submit an online IT request to gain access to these labs at http://www1.infotech.monash.edu.au/webservices/servicedesk/requestform/
|0||21/02/11||FIT5167 Moodle site is open for "guests". There is a self-assessed test on basic maths and statistics on Moodle. Please check this out before enrolling in this unit.|
|1||28/02/11||Introduction||Self-assessed test on basic maths and statistics|
|2||07/03/11||Artificial Neural Networks: an Overview|
|3||14/03/11||Perceptron for Linear Pattern Classification|
|4||21/03/11||Neural Networks for Non-linear Pattern Recognition 1|
|5||28/03/11||Neural Networks for Non-linear Pattern Recognition 2|
|6||04/04/11||Generalisation and Improving Neural Networks Performance|
|7||11/04/11||Unsupervised Classification with Self Organising Maps|
|8||18/04/11||Unit Test (in the lecture time slot - tute still on)||Unit Test during Week 8 lecture|
|Mid semester break|
|9||02/05/11||Associative Memory Networks||Assignment Stage 1 due Week 9 (hurdle)|
|10||09/05/11||Neural Networks for Time series Forecasting|
|11||16/05/11||Recurrent Networks for Time series Forecasting||Assignment Stage 2 due start Week 11 lecture|
|30/05/11||SWOT VAC||No formal assessment is undertaken SWOT VAC|
*Please note that these dates may only apply to Australian campuses of Monash University. Off-shore students need to check the dates with their unit leader.
To pass a unit which includes an examination as part of the assessment a student must obtain:
If a student does not achieve 40% or more in the unit examination or the unit non-examination total assessment, and the total mark for the unit is greater than 50% then a mark of no greater than 49-N will be recorded for the unit
Details will be provided.
Details will be provided.
Assignment coversheets are available via
"Student Forms" on the Faculty website: http://www.infotech.monash.edu.au/resources/student/forms/
You MUST submit a completed coversheet with all assignments, ensuring that the plagiarism declaration section is signed.
You must negotiate any extensions formally with your campus unit leader via the in-semester special consideration process: http://www.infotech.monash.edu.au/resources/student/equity/special-consideration.html.
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