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Monash University

FIT5047 Intelligent systems - Semester 1, 2015

This unit introduces the main problems and approaches to designing intelligent software systems including automated search methods, knowledge representation and reasoning, planning, reasoning under uncertainty, machine learning paradigms, and evolutionary algorithms.

Mode of Delivery

Caulfield (Day)

Workload Requirements

Minimum total expected workload equals 12 hours per week comprising:

(a.) Contact hours for on-campus students:

  • Two hours of lectures
  • One 2-hour laboratory

(b.) Additional requirements (all students):

  • A minimum of 8 hours independent study per week for completing lab and project work, private study and revision.

See also Unit timetable information

Unit Relationships




FIT9131 or FIT5131 or FIT9017 or equivalent
Fundamental math with introductory knowledge of probability

Chief Examiner

Campus Lecturer


Ingrid Zukerman

Consultation hours: Wed 3-4



Muhammad Amar

Andisheh Partovi

Ehsan Shareghi

Han Phan

Sadegh Kharazmi

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 the Student Evaluation of Teaching and Units (SETU) survey. 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, see:

www.monash.edu.au/about/monash-directions/ and on student evaluations, see: www.policy.monash.edu/policy-bank/academic/education/quality/student-evaluation-policy.html

Previous Student Evaluations of this Unit

Assessment weighting has been changed due to students' feedback.

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

Academic Overview

Learning Outcomes

At the 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;
  • models and approaches to building intelligent software systems;
  • different software toolkits and development environments;
  • current research trends in the field.
Developed attitudes that enable them to:
  • foster critical and independent analysis of how intelligent techniques can be used to enhance software applications and the development of smart environments.
Developed the skills to:
  • design and develop intelligent applications;
  • select and apply appropriate tools for a particular application.

Unit Schedule

Week Activities Assessment
0   No formal assessment or activities are undertaken in week 0
1 Introduction to Artificial Intelligence  
2 Problem Solving I  
3 Problem Solving II Assignment 1 handed out
4 Game playing, Knowledge representation -- logic I  
5 Knowledge representation -- logic II Assignment 2 handed out
6 Probability and Bayesian networks I  
7 Bayesian Networks II Assignment 1 due
8 Supervised Machine Learning I  
9 Supervised Machine Learning II Assignment 2 due and Assignment 3 handed out
10 Unsupervised Machine Learning  
11 Topics in Language Technology I  
12 Topics in Language Technology II Assignment 3 due
  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 learning system.

Teaching Approach

Lecture and tutorials or problem classes
This teaching and learning approach provides facilitated learning, practical exploration and peer learning.

Assessment Summary

Examination (3 hours): 70%; In-semester assessment: 30%

Assessment Task Value Due Date
Assignment 1 - Problem Solving 12% Week 7
Assignment 2 - Knowledge representation and Bayesian Networks 9% Week 9
Assignment 3 - Machine Learning 9% Week 12
Examination 1 70% To be advised

Assessment Requirements

Assessment Policy

Assessment Tasks


  • Assessment task 1
    Assignment 1 - Problem Solving
    A problem solving exercise on problem solving and knowledge representation.
    Criteria for assessment:

    Correctness and completeness of answers to problems.

    Due date:
    Week 7
  • Assessment task 2
    Assignment 2 - Knowledge representation and Bayesian Networks
    Representing information in predicate logic and Bayesian networks
    Criteria for assessment:

    Correctness and completeness of submitted answers and/or Bayesian networks.

    Due date:
    Week 9
  • Assessment task 3
    Assignment 3 - Machine Learning
    A problem solving exercise on machine learning.
    Criteria for assessment:

    Correctness and completeness of answers to machine learning problems.

    Due date:
    Week 12


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

Learning resources

Monash Library Unit Reading List (if applicable to the unit)

Feedback to you

Types of feedback you can expect to receive in this unit are:

  • Informal feedback on progress in labs/tutes
  • Graded assignments with comments
  • Other: Solutions to tutorials and labs

Extensions and penalties

Returning assignments

Resubmission of assignments

No resubmissions.

Referencing requirements

See Library Guides for Citing and Referencing at http://guides.lib.monash.edu/content.php?pid=88267&sid=656564

Assignment submission

It is a University requirement (http://www.policy.monash.edu/policy-bank/academic/education/conduct/student-academic-integrity-managing-plagiarism-collusion-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 electronic submission). Please note that it is your responsibility to retain copies of your assessments.

Online submission

If Electronic Submission has been approved for your unit, please submit your work via the learning system for this unit, which you can access via links in the my.monash portal.

Required Resources

Please check with your lecturer before purchasing any Required Resources. Limited copies of prescribed texts are available for you to borrow in the library, and prescribed software is available in student labs.

Netica (free)

Netlogo (free)

Weka Data Mining Toolkit (free)

Web access

Prescribed text(s)

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

Russell, S and Norvig, P. (2010). Artificial Intelligence - A Modern Approach. (3rd Edition) Prentice-Hall.

Recommended text(s)

Witten, I and Frank, E. (2005). Data Mining - Practical Machine Learning Tools and Techniques. (3rd Edition) Elsevier.

Korb, K and Nicholson, A. (2011). Bayesian Artificial Intelligence. (2nd Edition) CRC Press.

Other Information


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: www.policy.monash.edu.au/policy-bank/academic/education/index.html

Faculty resources and policies

Important student resources including Faculty policies are located at http://intranet.monash.edu.au/infotech/resources/students/

Graduate Attributes Policy

Student Charter

Student services

Monash University Library

Disability Liaison Unit

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.

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