Part A: Course Overview

Course Title: Artificial Intelligence

Credit Points: 12.00


Course Coordinator: Prof. Sebastian Sardina

Course Coordinator Phone: +61 3 9925 9824

Course Coordinator Email: sebastian.sardina@rmit.edu.au

Course Coordinator Location: 14.08.7D

Course Coordinator Availability: By appointment.


Pre-requisite Courses and Assumed Knowledge and Capabilities

Enforced Pre-Requisite courses:
Successful completion of the following course/s:

Note: it is a condition of enrolment at RMIT that you accept responsibility for ensuring that you have completed the prerequisite/s and agree to concurrently enrol in co-requisite courses before enrolling in a course. 

For information go to RMIT Course Requisites webpage. 

If you have completed prior studies at RMIT or another institution that developed the skills and knowledge covered in the above course/s you may be eligible to apply for credit transfer. Alternatively, if you have prior relevant work experience that developed the skills and knowledge covered in the above course/s you may be eligible for recognition of prior learning. Please follow the link for further information on how to apply for credit for prior study or experience. 


Course Description

This course introduces you to the basic concepts and techniques of Artificial Intelligence (AI). AI is the sub-area of computer science devoted to creating software and hardware to get computers to do things that would be considered ‘intelligent’ as if people did them. Artificial intelligence has had an active and exciting history and is now a reasonably mature area of computer science. Many of the research discoveries have now reached the point of industrial application and. many companies have made and saved millions of dollars by exploiting the results of AI research. However the goal of emulating human intelligence has not been reached and many stimulating and challenging problems remain.

All serious programmers and software engineers should know about the major AI techniques, which are regarded by many the core knowledge of any Computer Science degree. This course will allow you to gain generic problem solving skills that have applicability to a wide range of real-world problems. Topics covered include search strategies for solving problems, knowledge representation, automated planning, intelligent agents, reasoning under uncertainty, bio-inspired optimisation, and machine learning.


Objectives/Learning Outcomes/Capability Development

For more information on the program learning outcomes for your program, please see the program guide.


Upon successful completion of this course, you will be able to:

  1. Describe the key components of the artificial intelligence (AI) field and its relation and role in Computer Science;
  2. Identify and describe artificial intelligence techniques, including search heuristics, knowledge representation, automated planning and agent systems, machine learning, and probabilistic reasoning;
  3. Identify and apply AI techniques to a wide range of problems, including complex problem solving via search, knowledge-base systems, machine learning, probabilistic models, agent decision making, etc.;
  4. Design and implement appropriate AI solution techniques for such problems;
  5. Analyse and understand the computational trade-offs involved in applying different AI techniques and models.
  6. Communicate clearly and effectively using the technical language of the field correctly.


Overview of Learning Activities

You will be actively engaged in a range of learning activities such as lectorials, tutorials, practicals, laboratories, seminars, project work, class discussion, individual and group activities. Delivery may be face to face, online or a mix of both.

The learning activities included in this course are:

  • Lectorials, where key AI concepts and syllabus material will be presented and put in context, and targeted exercise and quizzes will be given to solve and discuss interactively during the session;
  • Reading material and online videos covering the syllabus material will be made available for students to process, usually before the corresponding lectorial; and
  • workshops/tutorials/labs/group discussions (including online forums) focused on projects and problem solving will provide practice in the application of AI theory and procedures, allow exploration of concepts with teaching staff and other students, and give feedback on your progress and understanding.


Overview of Learning Resources

RMIT will provide you with resources and tools for learning in this course through myRMIT Studies Course. 

There are services available to support your learning through the University Library. The Library provides guides on academic referencing and subject specialist help as well as a range of study support services. For further information, please visit the Library page on the RMIT University website and the myRMIT student portal.


Overview of Assessment

This course has no hurdle requirements.

Assessment tasks

Assessment Component 1: Assignments
Weighting 50%
This assessment task supports CLOs 3, 4, 5, & 6

Assessment Component 2: Semester Exercise Challenges (Timed and Timetabled Class Assessment)
Weighting 10%
This assessment task supports CLOs 1, 2, 3, 4, & 6
This assessment is a timed and timetabled assessment that students must attend on campus.

Assessment Component 3: Final Exercise Challenges (Timed and Timetabled Class Assessment)
Weighting 40%
This assessment task supports CLOs 1, 2, 3, 5, & 6
This assessment is a timed and timetabled assessment that students must attend on campus.

If you have a long-term medical condition and/or disability it may be possible to negotiate to vary aspects of the learning or assessment methods. You can contact the program coordinator or Equitable Learning Services if you would like to find out more.