Faculty of Information Technology

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This unit entry is for students who completed this unit in 2016 only. For students planning to study the unit, please refer to the unit indexes in the the current edition of the Handbook. If you have any queries contact the managing faculty for your course or area of study.

Monash University

6 points, SCA Band 2, 0.125 EFTSL

Postgraduate - Unit

Refer to the specific census and withdrawal dates for the semester(s) in which this unit is offered.


Information Technology



  • First semester 2016 (Day)


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.


At the completion of this unit, students should be able to:

  1. explain the theoretical foundations of Artificial Intelligence (AI) - such as the Turing test, Rational Agency and the Frame Problem - that underpin the application to information technology and society;
  2. critically explain, evaluate and apply appropriate AI theories, models and/or techniques in practice - including logical inference, heuristic search, genetic algorithms, supervised and unsupervised machine learning and Bayesian inference;
  3. utilise appropriate software tools to develop AI models or software;
  4. utilise and explain evaluation criteria to measure the correctness and/or suitability of models.


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

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

Chief examiner(s)


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



Additional information on this unit is available from the faculty at: