Unit Outline

The unit provides an introduction to many AI sub-fields, including: expert systems, machine learning, natural language processing, computer vision, intelligent agents. Students will be exposed to state-of-the-art examples as well as emerging technologies and get practical experience of solving interesting problems in each of these sub-fields. The unit covers the definition of Artificial Intelligence and its subfields; introduces foundational logic and knowledge representation; and considers social, ethical, and philosophical consequences of the theory and practice of AI. Students will learn different perspectives on the approach to creating AI, its purpose, and its validity. The unit will highlight the increasingly important social and economic roles of AI and will feature guest lectures from research and/or industry experts to highlight current research directions and deepen students' understanding of specific topics.

Intended Learning Outcomes

As per the Assessment and Results Policy 1.3, your results will reflect your achievement against specified learning outcomes.

On completion of this unit, you will be able to:


1.	Explain the fundamental concepts of Artificial Intelligence (AI) and its application in society currently and in the future
2.	Analyse and apply techniques to develop AI solutions to solve practical problems.
3.	Explain the human intelligence approaches behind various AI techniques

Alterations as a result of student feedback

The unit has been revised to address the requirements for a balance between conceptual and practical content based on student feedback.

Teaching arrangements


ATTENDANCE MODE	TEACHING TYPE	LEARNING ACTIVITY	CONTACT HOURS	FREQUENCY
On Campus	Lecture (Online)	A real-time (i.e. synchronous) interactive activity involving the whole class whose primary purpose is the presentation and structuring of information/ideas/skills to facilitate student learning. All students are expected to attend.	2	Weekly
On Campus	Computer Laboratory	A structured real-time (i.e. synchronous) computer-based activity in a small-group setting where the primary purpose is the clarification, exploration or reinforcement of subject content presented or accessed at another time or place (e.g. lecture, preparatory work). It is teacher supported and may involve student-teacher and/or student-student interaction and dialogue for achievement of its learning outcomes. The students enrolled in the class are expected to attend.	2	Weekly

Attendance / engagement expectations

If your unit is offered On campus, it is expected that you will attend all on-campus and onsite learning activities. This is to support your own learning and the development of a learning community within the unit. If you are unable to attend regularly, please discuss the situation with your course coordinator and/or our UConnect support team.

If your unit is offered Online, it is expected you will engage in all those activities as indicated in the Unit Outline, including any self-directed learning.

If you miss a learning activity for a legitimate reason (e.g., illness, carer responsibilities) teaching staff will attempt to provide alternative activities (e.g., make up readings) where it is possible.

How will I be Assessed?

For more detailed assessment information please see MyLO.

Assessment schedule


ASSESSMENT TASK #	ASSESSMENT TASK NAME	DATE DUE	WEIGHT	LINKS TO INTENDED LEARNING OUTCOMES
Assessment Task 1:	Software Application	Week 13	30 %	LO1, LO2, LO3
Assessment Task 2:	Examination	Exam Period	40 %	LO1, LO2, LO3
Assessment Task 3:	Quizzes (weekly)	Refer to Assessment Description	15 %	LO1, LO2, LO3
Assessment Task 4:	Tutorial Exercises	Refer to Assessment Description	15 %	LO1, LO2, LO3

Assessment details

Assessment Task 1: Software Application

Task Description:

This product-based assessment evaluates student's understanding of AI techniques and how to use them to develop an application. In this assignment, students will be asked to analyse a given dataset and use AI tools to apply relevant AI techniques to solve a real-world problem. This assignment must be completed and submitted to MyLO by the end of Week 13. A submission must include: (1) A report file that demonstrates the methods used in the assignment, and (2) all source code files for the application.

The assignment will include a milestone check during the semester to ensure progression.

Task Length:

Software application

Due Date:

Week 13

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Identify the abilities of AI to solve a real-world problem.	LO1
2	Analyse and process data using AI tools	LO2
3	Design and implement AI approaches to work with the data	LO2
4	Select the most suitable AI approach for an application	LO2, LO3

Assessment Task 2: Examination

Task Description:

The final exam will be conducted online using MyLO. The exam will examine your knowledge and understanding of the various AI techniques reviewed in the unit. The exam will take 2 hours during the exam period.

Task Length:

2 hours

Due Date:

Exam Period

Weight:

40 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain the use of AI in different problems, applications	LO1
2	Explain AI techniques for acquiring, representing, and reasoning with data, information, and knowledge	LO1, LO3
3	Analyse the application of relevant AI techniques to a problem/application	LO2
4	Explain the advantages and disadvantages of AI techniques	LO1, LO3

Assessment Task 3: Quizzes (weekly)

Task Description:

This assessment consists of weekly problem-based learning to help students understand AI and Human intelligence covered by the lecture materials throughout the semester.

Task Length:

20 minutes (weekly)

Due Date:

Refer to Assessment Description

Weight:

15 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain the general concepts of Artificial Intelligence	LO1
2	Select relevant Artificial Intelligence techniques to process knowledge and data.	LO2
3	Apply AI techniques to solve a problem	LO2
4	Explain the human intelligence approach behind different AI paradigms	LO3

Assessment Task 4: Tutorial Exercises

Task Description:

This test-based assessment evaluates student's ability to apply what has been learnt to solve practical problems. This is individual work and students will be required to do it in their tutorial classes. The task consists of 10 weekly programming exercises.

Task Length:

90 minutes (weekly)

Due Date:

Refer to Assessment Description

Weight:

15 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Identify a range of practical problems that AI can solve	LO1, LO2
2	Analyse relevant AI approaches to work with a specific dataset	LO2
3	Design and implement AI approaches to solve a practical problem	LO2, LO3

How your final result is determined

To pass this unit, you need to demonstrate your attainment of each of the Intended Learning Outcomes, achieve a final unit grade of 50% or greater, and pass any hurdle tasks.

Submission of assignments

Where practicable, assignments should be submitted to an assignment submission folder in MYLO. You must submit assignments by the due date or receive a penalty (unless an extension of time has been approved by the Unit Coordinator). Students submitting any assignment in hard copy, or because of a practicum finalisation, must attach a student cover sheet and signed declaration for the submission to be accepted for marking.

Requests for extensions

If you are unable to submit an assessment task by the due date, you should apply for an extension.

A request for an extension should first be discussed with your Unit Coordinator or teaching support team where possible. A request for an extension must be submitted by the assessment due date, except where you can provide evidence it was not possible to do so. Typically, an application for an extension will be supported by documentary evidence: however, where it is not possible for you to provide evidence please contact your Unit Coordinator.

The Unit Coordinator must notify you of the outcome of an extension request within 3 working days of receiving the request.

Late penalties

Assignments submitted after the deadline will receive a late penalty of 5% of the original available mark for each calendar day (or part day) that the assignment is late. Late submissions will not be accepted more than 10 calendar days after the due date, or after assignments have been returned to other students on a scheduled date, whichever occurs first. Further information on Late Penalties can be found on the Assessments and Results Procedure.

Review of results and appeals

You are entitled to ask for a review of the marking and grading of your assessment task if there is an irregularity in the marking standards or an error in the process for determining the outcome of an assessment. Details on how to request a review of a mark for an assignment are outlined in the Review and Appeal of Academic Decisions Procedure.

Required Resources

Required reading materials

N/A