Artificial Intelligence (COMP30024)
Undergraduate level 3Points: 12.5On Campus (Parkville)
Overview
| Availability | Semester 1 |
|---|---|
| Fees | Look up fees |
AIMS
Artificial intelligence is the quest to create intelligent agents that can complete complex tasks which are at present only achievable by humans. This broad field covers logic, probability, perception, reasoning, learning and action; and everything from Mars Rover robotic explorers to the Watson Jeopardy playing program. You will explore some of the vast area of artificial intelligence. Topics covered include: searching, problem solving, reasoning, knowledge representation and machine learning. Topics may also include some of the following: game playing, expert systems, pattern recognition, machine vision, natural language, robotics and agent-based systems.
INDICATIVE CONTENT
- Agents and search
- Probabilistic reasoning
- Reinforcement Learning
- Pattern recognition for robotics.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
- Identify problems that can be solved by search, and create search-based solution algorithms
- Design intelligent agents
- Choose the best search-based solving methods for a particular problem
- Make use of formal approaches for representing and reasoning about knowledge
- Build systems that use simple learning approaches to improve their performance
Generic skills
On completion of this subject students should have developed the following generic skills:
- The ability to analyse and solve problems involving complex reasoning
- The ability to synthesise information and communicate results effectively
- The capacity for critical and independent thought and reflection
- The ability to apply knowledge of basic science and engineering fundamentals
- The ability to undertake problem identification, formulation and solution.
Last updated: 14 March 2025
Eligibility and requirements
Prerequisites
One of the following:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP20003 | Algorithms and Data Structures | Semester 2 (On Campus - Parkville) |
12.5 |
| COMP20007 | Design of Algorithms | Semester 1 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
Students cannot enrol in and gain credit for this subject and:
433-303 Artificial Intelligence
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 14 March 2025
Assessment
Additional details
- A programming project submitted in two parts during semester, requiring approximately 30 - 35 hours of work (30%). A component of the marks for the project work will be based on the individual contribution to the project.
- 3-hour end-of-semester written examination (70%).
Hurdle requirement: To pass the subject, students must obtain at least 50% overall
- 15/30 in project work
- And 35/70 in the written examination.
Intended Learning Outcomes (ILOs) 1-4 are addressed in the lectures, workshops exercises and the examination.
ILO 5 is addressed in the project work.
Last updated: 14 March 2025
Dates & times
- Semester 1
Principal coordinator Christopher Leckie Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week Total time commitment 170 hours Teaching period 27 February 2017 to 28 May 2017 Last self-enrol date 10 March 2017 Census date 31 March 2017 Last date to withdraw without fail 5 May 2017 Assessment period ends 23 June 2017 Semester 1 contact information
Prof Chris Leckie
email: caleckie@unimelb.edu.au
Time commitment details
170 hours
Last updated: 14 March 2025
Further information
- Texts
- Subject notes
INDICATIVE KEY LEARNING RSEOURCES
Students have access to lecture notes, lecture slides, tutorial exercises, and a test environment for evaluating their project submissions.
CAREERS/INDUSTRY LINKS
The material in this subject is highly relevant to the growing industry of data analytics in fields such as medicine, computer gaming, finance and industrial automation. Examples of guest lecturers who have been involved in this subject include staff of Telstra, IBM and NICTA.
- Related Handbook entries
This subject contributes to the following:
Type Name Informal specialisation Computer Science Informal specialisation Master of Engineering (Software with Business) Specialisation (formal) Software with Business Informal specialisation Computer Science Informal specialisation Master of Engineering (Software) Major Computer Science Informal specialisation Science-credited subjects - new generation B-SCI and B-ENG. Specialisation (formal) Software - Breadth options
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
Last updated: 14 March 2025