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.
CSE5301
Grace Rumantir
Grace Rumantir
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 |
Monash is committed to excellence in education and regularly seeks feedback from students, employers and staff. One of the key formal ways students have to provide feedback is through SETU, Student Evaluation of Teacher and Unit. The University's student evaluation policy requires that every unit is evaluated each year. Students are strongly encouraged to complete the surveys. The feedback is anonymous and provides the Faculty with evidence of aspects that students are satisfied and areas for improvement.
For more information on Monash's educational strategy, and on student evaluations, see:
http://www.monash.edu.au/about/monash-directions/directions.html
http://www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html
If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp
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/
Scientific Calculator
| Week | Date* | Activities | Assessment |
|---|---|---|---|
| 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 |
| 12 | 23/05/11 | Revision | |
| 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.
Submission must be made by the due date otherwise penalties will be enforced.
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.
Monash has educational policies, procedures and guidelines, which are designed to ensure that staff and students are aware of the University's academic standards, and to provide advice on how they might uphold them.
You can find Monash's Education Policies at:
http://policy.monash.edu.au/policy-bank/academic/education/index.html
Key educational policies include:
The University provides many different kinds of support services for you. Contact your tutor if you need advice and see the range of services available at www.monash.edu.au/students The Monash University Library provides a range of services and resources that enable you to save time and be more effective in your learning and research. Go to http://www.lib.monash.edu.au or the library tab in my.monash portal for more information. Students who have a disability or medical condition are welcome to contact the Disability Liaison Unit to discuss academic support services. Disability Liaison Officers (DLOs) visit all Victorian campuses on a regular basis
Recommended Reading