Artificial Intelligence

This subject has the following teaching availabilities in 2016:

170 hours

One of the following:

433-253 Algorithms and Data Structures

433-298 Algorithms and Data Structures

None

Students cannot enrol in and gain credit for this subject and:

433-303 Artificial Intelligence

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

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
• Machine Learning
• Pattern recognition for robotics.

INTENDED LEARNING OUTCOMES (ILO)

On completion of this subject the student is expected to:

1. Identify problems that can be solved by search, and create search-based solution algorithms
2. Design intelligent agents
3. Choose the best search-based solving methods for a particular problem
4. Make use of formal approaches for representing and reasoning about knowledge
5. Build systems that use simple learning approaches to improve their performance

• 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.

Artificial Intelligence: A Modern Approach, by S. Russell and P. Norvig, Pearson, ISBN 9781292024202.

This subject potentially can be taken as a breadth subject component for the following courses:

• Bachelor of Arts
• Bachelor of Commerce
• Bachelor of Music

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.

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.

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.