This unit introduces main techniques widely used in intelligent software systems to students in the Master of Information Technology Systems course with the Network Computing major. Specifically, it focuses on the techniques in relation to network structures. Main topics covered include neural network models, supervised learning and classification, unsupervised learning and clustering, fuzzy logic, intelligent decision analysis, optimum network flow modelling, and recommender systems.
2 hrs lectures/wk, 2 hrs laboratories/wk
Students will be expected to spend a total of 12 hours per week during semester on this unit as follows:
Lectures: 2 hours per week
Tutorials/Lab Sessions: 2 hours per week per tutorial
and up to an additional 8 hours in some weeks for completing lab and project work, private study and revision.
Fundamental mathematics
Examination (3 hours): 70%; In-semester assessment: 30%
| Assessment Task | Value | Due Date |
|---|---|---|
| Solving A Neural Network Problem | 30% | 14 September 2012 |
| Examination 1 | 70% | 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
| Week | Activities | Assessment |
|---|---|---|
| 0 | No formal assessment or activities are undertaken in week 0 | |
| 1 | Introduction to Intelligent Systems and Neural Networks | |
| 2 | Neuron Learning and Perceptrons | |
| 3 | Multilayered Networks | |
| 4 | Supervised Learning - Backpropagation Learning Rule | |
| 5 | Classification and Prediction with Case Studies | |
| 6 | Unsupervised Learning - Clustering with Self-Organisation | |
| 7 | Unsupervised Learning with Adaptive Resonance Theory | Assignment proposal due |
| 8 | Data Mining and Knowledge Discovery | |
| 9 | Other Intelligent Techniques | |
| 10 | Fuzzy Logic | |
| 11 | Business Intelligence Modelling - Decision Analysis under Uncertainty | |
| 12 | Decision Trees, Decision Making Using Sample Information; Revision and Exam Preparation | Assignment due |
| SWOT VAC | No formal assessment is undertaken during SWOT VAC | |
| Examination period | LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/ academic/education/assessment/ assessment-in-coursework-policy.html |
*Unit Schedule details will be maintained and communicated to you via your MUSO (Blackboard or Moodle) learning system.
Faculty Policy - Unit Assessment Hurdles (http://www.infotech.monash.edu.au/resources/staff/edgov/policies/assessment-examinations/unit-assessment-hurdles.html)
Academic Integrity - Please see the Demystifying Citing and Referencing tutorial at http://lib.monash.edu/tutorials/citing/
The assessment will be based on both contents and presentation. You must get permission from your tutor for the problem you choose to do. You are expected to train your network and perform some sort of analysis of the results. The more analysis you do, the more insight you will gain into the problem and the technique (and the more marks you will receive).
It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/plagiarism-procedures.html) for students to submit an assignment coversheet for each assessment item. Faculty Assignment coversheets can be found at http://www.infotech.monash.edu.au/resources/student/forms/. Please check with your Lecturer on the submission method for your assignment coversheet (e.g. attach a file to the online assignment submission, hand-in a hard copy, or use an online quiz).
If Electronic Submission has been approved for your unit, please submit your work via the Moodle site for this unit, which you can access via links in the my.monash portal.
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. For Sunway see http://www.monash.edu.my/Student-services, and for South Africa see http://www.monash.ac.za/current/
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. At Sunway, visit the Library and Learning Commons at http://www.lib.monash.edu.my/. At South Africa visit http://www.lib.monash.ac.za/.
Academic support services may be available for students who have a disability or medical condition. Registration with the Disability Liaison Unit is required. Further information is available as follows:
Recommended reading will be provided on the unit Moodle site.