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FIT5186 Intelligent systems - Semester 2, 2012

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.

Contact Hours

2 hrs lectures/wk, 2 hrs laboratories/wk

Workload

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.

Unit Relationships

Prerequisites

Fundamental mathematics

Chief Examiner

Campus Lecturer

Academic Overview

Outcomes

On completion of this unit students will have a knowledge and understanding of:
  • the applications of intelligent software systems;
  • the principles and theoretical underpinning of intelligent software systems;
  • the models and approaches to building intelligent software systems;
  • the advantages and limitations of intelligent models and approaches for solving a wide range of practical problems;
  • different software toolkits and development environments;
  • current research trends in the field.

Graduate Attributes

Monash prepares its graduates to be:
  1. responsible and effective global citizens who:
    1. engage in an internationalised world
    2. exhibit cross-cultural competence
    3. demonstrate ethical values
  2. critical and creative scholars who:
    1. produce innovative solutions to problems
    2. apply research skills to a range of challenges
    3. communicate perceptively and effectively

Assessment Summary

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

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach helps students to initially encounter information at lectures, discuss and explore the information during tutorials, and practice in a hands-on lab environment.

Feedback

Our feedback to You

Types of feedback you can expect to receive in this unit are:
  • Graded assignments with comments
  • Solutions to tutes, labs and assignments

Your feedback to Us

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

Previous Student Evaluations of this unit

If you wish to view how previous students rated this unit, please go to
https://emuapps.monash.edu.au/unitevaluations/index.jsp

Unit Schedule

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.

Assessment Requirements

Assessment Policy

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/

Assessment Tasks

Participation

  • Assessment task 1
    Title:
    Solving A Neural Network Problem
    Description:
    In this assignment, you will be applying what you have learnt about neural networks and the backpropagation learning algorithm to a forecasting, prediction or classification problem of your choice. You are required to write up your findings in the form of a short (4-6 pages) conference-type paper. When you have identified your problem, you need to write a one-page proposal which outlines your problem, where you will get your data set, and the methodology you will use. This needs to be handed out to your tutor for approval during your tutorial in Week 7.
    Weighting:
    30%
    Criteria for assessment:

    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).

    Due date:
    14 September 2012

Examinations

  • Examination 1
    Weighting:
    70%
    Length:
    3 hours
    Type (open/closed book):
    Closed book
    Electronic devices allowed in the exam:
    Non-programmable calculators

Assignment submission

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).

Online submission

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.

Extensions and penalties

Returning assignments

Other Information

Policies

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. 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:

  • Website: http://monash.edu/equity-diversity/disability/index.html;
  • Email: dlu@monash.edu
  • Drop In: Equity and Diversity Centre, Level 1 Gallery Building (Building 55), Monash University, Clayton Campus, or Student Community Services Department, Level 2, Building 2, Monash University, Sunway Campus
  • Telephone: 03 9905 5704, or contact the Student Advisor, Student Commuity Services at 03 55146018 at Sunway

Reading list

Recommended reading will be provided on the unit Moodle site.