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Previous Student Evaluations of this unit

If you wish to view how previous students rated this unit, please go to

Required Resources

You will need access to a Neural Network tool such as:

  • Matlab 2009a with Neural Network Toolbox
  • Emergent (available free from http://grey.colorado.edu/emergent/index.php/Main_Page)
  • SNNS (available free from www.ra.cs.uni-tuebingen.de/SNNS)

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/

Examination material or equipment

Scientific Calculator

Unit Schedule

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.

Assessment Policy

To pass a unit which includes an examination as part of the assessment a student must obtain:

  • 40% or more in the unit's examination, and
  • 40% or more in the unit's total non-examination assessment, and
  • an overall unit mark of 50% or more.

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

Assessment Tasks


  • Assessment task 1
    Unit Test
    Closed book
    Criteria for assessment:

    Details will be provided.

    Due date:
    Week 8 lecture
    The unit test will be conducted during the Week 8 lecture time slot.  Week 8 tutorials will still run as per normal.
  • Assessment task 2
    Applications of Neural Network Algorithms
    Students are to build neural network models for a given data set and provide analysis thereof.
    Criteria for assessment:

    Details will be provided.

    Due date:
    Stage 1 due Week 9 (hurdle), Stage 2 due start Week 11 lecture
    The assignment is to be submitted at the start of the Week 11 lecture.  Penalty for late submission applies.


  • Examination 1
    60 %
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    Scientific Calculator

Assignment submission

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.

Extensions and penalties

Returning assignments


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:

Key educational policies include:

Student services

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

  • S. Samarasinghe, Neural Networks for Applied Sciences and Engineering: From Fundamentals to Complex Pattern Recognition, Auerbach Publications, 2007 (e-book from Monash Library)
  • G. Dreyfus, Neural Networks: Methodology and Applications, Springer-Verlag Berlin Heidelberg, 2005 (e-book)
  • R. Beale, Neural Computing: an Introduction, Institute of Physics Pub., Bristol, 1991 (e-book)
  • S. Haykin, Neural Networks and Learning Machines, 3rd edition, Prentice Education , Inc., New Jersey, 2009
  • C. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 2005
  • J. Freeman and D. Skapura, Neural Networks: Algorithms, Applications, and Programming Techniques, Addison-Wesley, Massachussets, 1991
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