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Monash University

FIT4009 Advanced topics in intelligent systems - Semester 2, 2012

Methods from Artificial Intelligence (AI) form the basis for many advanced information systems. These techniques address problems that are difficult to solve or not efficiently solvable with conventional techniques. Building on the undergraduate curriculum this unit introduces the student to advanced AI methods and their applications in information systems.

Mode of Delivery

Clayton (Day)

Contact Hours

2 hrs lectures/wk


For on-campus students, workload commitments are: (12 hours per week total)

  • Lectures: 2 hours per week
  • Reading, preparation, assignment work, revision: 10 hours per week

Unit Relationships


Completion of the Bachelor of Computer Science or equivalent to the entry requirements for the Honours program. Students must also have enrolment approval from the Honours Coordinator.

Chief Examiner

Campus Lecturer


Gholamreza Haffari

David Dowe

Academic Overview


At the completion of this unit students will have:
  • achieved an overview of different technologies that form the basis of intelligent information systems;
  • understood the capabilities of these methods;
  • learned to recognise tasks that can be solved with these methods;
  • the ability to judge the limitations of these methods.With successful completion of the unit the students;
  • the ability to apply the standard techniques in the chosen sub-fields of intelligent information systems to the construction and design of such systems;
  • the ability to critically evaluate the performance of these approaches;
  • the ability to compare these techniques to alternative approaches;
  • gained an appreciation of the practical relevance of intelligent information systems.

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

Assignment and Examination, relative weight depending on topic composition. When no exam is given students will be expected to demonstrate their knowledge by solving practical problems and maybe required to give an oral report.

Assessment Task Value Due Date
Assignment 1 - Supervised Learning 15% Week 4
Assignment 2 - Parametric Methods, Clustering 15% Week 6
Assignment 3 - MML modelling 30% Week 11, Thursday, 11 October 2012
Examination 1 40% To be advised

Teaching Approach

Problem-based learning
Students are encouraged to take responsibility for organising and directing their learning with support from their lecturers.


Our feedback to You

Types of feedback you can expect to receive in this unit are:
  • Graded assignments without comments
  • Interviews

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:

Previous Student Evaluations of this unit

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

Prescribed text(s)

Limited copies of prescribed texts are available for you to borrow in the library.

C. S. Wallace. (2005). Statistical and Inductive Inference by Minimum Message Length. () Springer (ISBN: 0-387-23795-X).

Ethem ALPAYDIN. (2010). Introduction to Machine Learning. () The MIT Press.

Unit Schedule

Week Activities Assessment
0   No formal assessment or activities are undertaken in week 0
1 Unit introduction, Introduction to Machine Learning  
2 Supervised Learning (PAC theory, ...) Assignment 1 released Week 2
3 Parametric Methods (maximum likelihood, bias-variance, ...)  
4 Clustering (mixture models, k-means, ..) Assignment 1 due Week 4; Assignment 2 released Week 4
5 non-parametric methods (k-nearest neighbor, ...)  
6 Decision Trees Assignment 2 due Week 6
7 Bayesianism, Minimum Message Length (MML), inference, prediction  
8 MML multinomial; MML clustering and mixture modelling  
9 MML decision trees (and graphs) and log-loss  
10 Neyman-Scott and related problems for Maximum Likelihood  
11 MML Bayesian nets, grammatical inference Assignment 3 due Week 11, Thursday, 11 October 2012
12 Algorithmic information theory, formal definitions of intelligence  
  SWOT VAC No formal assessment is undertaken in SWOT VAC
  Examination period LINK to Assessment Policy: http://policy.monash.edu.au/policy-bank/

*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


  • Assessment task 1
    Assignment 1 - Supervised Learning
    This will be a programming assignment.

    Further details will be provided in the assignment handout.
    Criteria for assessment:
    • How well solutions are explained.
    • Quality of code demonstrated where applicable.

    Further details will be provided in the assignment handout.

    Due date:
    Week 4
  • Assessment task 2
    Assignment 2 - Parametric Methods, Clustering
    This assignment will involve a set of written questions relating to the learning material.

    Further details will be provided in the assignment handout.
    Criteria for assessment:

    Quality of answers to questions, demonstrates understanding of the learning material.

    Further details will be provided in the assignment handout.

    Due date:
    Week 6
  • Assessment task 3
    Assignment 3 - MML modelling
    This will be a theory and programming assignment.

    Further details will be provided in the assignment handout.
    Criteria for assessment:
    • How well solutions are explained.
    • Quality of code demonstrated, where applicable.

    Further details will be provided in the assignment handout.

    Due date:
    Week 11, Thursday, 11 October 2012


  • Examination 1
    3 hours
    Type (open/closed book):
    Open book
    Electronic devices allowed in the exam:

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 VLE site for this unit, which you can access via links in the my.monash portal.

Extensions and penalties

Returning assignments

Other Information


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

Additional reading:

Pattern Recognition and Machine Learning, Chris Bishop, Springer, 2006.

D. L. Dowe (2011a), "MML, hybrid Bayesian network graphical models, statistical consistency, invariance and uniqueness", Handbook of the Philosophy of Science - (HPS Volume 7) Philosophy of Statistics, P.S. Bandyopadhyay and M.R. Forster (eds.), Elsevier, pp901-982, 1/June/2011 (accessible via www.csse.monash.edu.au/~dld/David.Dowe.publications.html#Dowe2011a)

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