Unit Outline

This unit is designed to give students an insight into a range of Artificial Intelligence (AI) techniques. AI is an emerging branch of Information and Communication Technology which has created an array of disruptions in multiple industries. The AI techniques leverage the computational power of machines to deal with complex tasks which normally require human intelligence. Students will learn the two main streams of AI including Knowledge-Driven AI and Data-Driven AI as well as current advanced AI techniques which have been used by giant tech companies like Google, Amazon, IBM and Microsoft. With the technologies discussed in the lecture, it brings together the state-of-the-art research and practical techniques in AI, providing students with the knowledge and capacity to conduct AI research and to develop AI applications. Students will have a chance to master advanced AI tools and APIs to explore and specialise their understanding, and also be required to use these technologies to develop an AI application. At the end of this unit, a student should understand the fundamental AI technologies and be able to provide design recommendations for a particular AI application.

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 concepts of Artificial Intelligence (AI) and its application in society currently and in the future
2.	Create and evaluate AI applications using AI development techniques and tools
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:	Assignment	Week 13	30 %	LO1, LO2, LO3
Assessment Task 2:	Examination	Exam Period	40 %	LO1, LO2, LO3
Assessment Task 3:	Weekly tests	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: Assignment

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:

Code

Due Date:

Week 13

Weight:

30 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Create and evaluate AI solutions by applying appropriate AI techniques	LO2, LO3
2	Analyse and process data using AI tools	LO2
3	Explain AI techniques relevant to the application	LO1, 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, LO3
2	Evaluate the application of relevant AI techniques to a problem/application	LO2
3	Explain AI concepts and techniques, including capabilities and limitations	LO1, LO3

Assessment Task 3: Weekly tests

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 Questions

Due Date:

Refer to Assessment Description

Weight:

15 %

CRITERION #

CRITERION

MEASURES INTENDED

LEARNING OUTCOME(S)


1	Explain fundamental concepts of AI	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	Solve AI practical problems by applying recognised AI techniques	LO2
2	Create reusable AI functions using AI tools	LO3
3	Explain AI concepts and human intelligence relevant to the practical problem	LO1, 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.