Note: This is an archived Handbook entry from 2014.
|Dates & Locations:|| |
This subject is not offered in 2014.
|Time Commitment:||Contact Hours: 48 hours, comprising of two 1-hour lectures and one 2-hour workshop per week |
Total Time Commitment:
One of the following:
Study Period Commencement:
433-253 Algorithms and Data Structures
433-298 Algorithms and Data Structures
|Recommended Background Knowledge:||None|
|Non Allowed Subjects:|| |
433-303 Artificial Intelligence
|Core Participation Requirements:||
For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Student Support and Engagement Policy, academic requirements for this subject are articulated in the Subject Overview, Learning Outcomes, Assessment and Generic Skills sections of this entry.
It is University policy to take all reasonable steps to minimise the impact of disability upon academic study, and reasonable adjustments will be made to enhance a student's participation in the University's programs. Students who feel their disability may impact on meeting the requirements of this subject are encouraged to discuss this matter with a Faculty Student Adviser and Student Equity and Disability Support: http://services.unimelb.edu.au/disability
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, logic and deduction, 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 planning, neural networks.
INTENDED LEARNING OUTCOMES (ILO)
On completion of this subject the student is expected to:
Hurdle requirement: To pass the subject, students must obtain at least 50% overall
A component of the marks for the project work will be based on the individual contribution to the project.
Intended Learning Outcomes (ILOs) 1-4 are addressed in the lectures, workshops exercises and examination.
ILO 5 is addressed in the project work.
|Prescribed Texts:|| |
|Breadth Options:|| |
This subject potentially can be taken as a breadth subject component for the following courses:
You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.
|Fees Information:||Subject EFTSL, Level, Discipline & Census Date|
On completion of this subject students should have developed the following generic skills:
INDICATIVE KEY LEARNING RSEOURCES
Students have access to lecture notes, lecture slides, tutorial exercises, and a test environment for evaluating their project submissions.
The material inthis subject is highly relevant to the growing industry of data analytics in fields such as medicine, computer gaming, finance and industrial auto material. Examples of guest lecturers who have been involved in this subject include staff of Telstra and NICTA.
Computing and Software Systems |
Master of Engineering (Software with Business)
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