Unit Guide

General Information

Unit convenor and teaching staff	Unit convenor and teaching staff Abhaya Nayak abhaya.nayak@mq.edu.au Lecturer/Tutor Manas Patra manas.patra@mq.edu.au Tutor Ben Woods Tutor Canh Thanh Nguyen
Credit points	Credit points 10
Prerequisites	Prerequisites 130cp at 1000 level or above including COMP2000 or COMP229 or COMP2010 or COMP225 or COMP2110 or COMP249 or COMP2160
Corequisites	Corequisites
Co-badged status	Co-badged status
Unit description	Unit description Artificial Intelligence (AI) is a well-established field that studies how computers and computer software capable of exhibiting intelligent behaviour can be designed. In this unit students will be exposed to fundamental concepts in AI such as agent architecture, knowledge representation, planning and search, as well as their application in some topical domains. Upon completion of this unit students will be able to apply problem-solving strategies that are required to build intelligent systems.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

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

ULO1: Describe the roles of various search techniques in AI and use appropriate tools to implement them.
ULO2: Explain and implement basics of supervised machine learning algorithms
ULO3: Explain biologically inspired algorithms and their roles in AI, and implement some such algorithms in different contexts including adversarial games.
ULO4: Describe the role that uncertainty plays in AI, and demonstrate ability for sound reasoning of different sorts from uncertain knowledge.

General Assessment Information

The assessment of this unit consists of two quizzes, two assignments and a final exam. The quizzes will be carried out online in iLearn. You will submit the solutions to the two assignments via iLearn by the due date. The form and date of the final examination will be announced later in the semester.

Late Assessment Submission Penalty

From 1 July 2022, Students enrolled in Session based units with written assessments will have the following late penalty applied. Please see https://students.mq.edu.au/study/assessment-exams/assessments for more information.

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark) will be applied each day a written assessment is not submitted, up until the 7^th day (including weekends). After the 7^th day, a grade of '0' will be awarded even if the assessment is submitted. Submission time for all written assessments is set at 11:55 pm. A 1-hour grace period is provided to students who experience a technical concern.

For any late submission of time-sensitive tasks, such as scheduled tests/exams, performance assessments/presentations, and/or scheduled practical assessments/labs, students need to submit an application for Special Consideration.

In this unit, late submissions will be accepted as follows:

Quiz 1: NO, unless Special Consideration is granted
Quiz 2: NO, unless Special Consideration is granted
Assignment 1: YES, Standard Late Penalty applies
Assignment 2: YES, Standard Late Penalty applies

Supplementary Exam

In general, if you receive Special Consideration for the final exam, a supplementary exam will be scheduled after the normal exam period, following the release of marks. By making a special consideration application for the final exam you are declaring yourself available for a resit during the supplementary examination period and will not be eligible for a second special consideration approval based on pre-existing commitments. Please ensure you are familiar with the policy prior to submitting an application. Approved applicants will receive an individual notification one week prior to the exam with the exact date and time of their supplementary examination.

Assessment Standards

COMP3160 will be assessed and graded according to the University assessment and grading policies.

The following general standards of achievement will be used to assess each of the assessment tasks with respect to the letter grades.

Pass: Can correctly reproduce facts and definitions across a breadth of concepts, but lacks depth of understanding. Can describe and/or employ search techniques in ways that are close to those discussed in lectures or notes. Can employ AI techniques to build a basic learning machine. Has basic understanding of biologically inspired algorithms and adversarial games. Has demonstrated some ability for sound reasoning in an uncertain domain.

Credit/Distinction: As for Pass plus: Exhibits breadth and depth of understanding of concepts. Can proficiently describe and/or employ search techniques going beyond how they were discussed in lectures or notes. Can employ AI techniques to a build very good learning machine. Has excellent understanding of biologically inspired algorithms and adversarial games, and can easily employ the former to develop strategies for the latter. Has excellent understanding of the role uncertainty plays in AI and demonstrated excellence for sound reasoning in uncertain domains.

High Distinction: As for Credit/Distinction plus: Is aware of the context in which the concepts are developed and their limitations. Can cogently describe in their own words and efficiently employ search techniques, going well beyond how they were discussed in lectures or notes. Can employ AI techniques to build an excellent learning machine. Has outstanding understanding of biologically inspired algorithms and adversarial games, and can easily employ the former to develop and evaluate strategies for the latter. Has excellent understanding of the role uncertainty plays in AI and has outstanding ability for sound reasoning in uncertain domains.

Assessment Process

These assessment standards will be used to give a numeric mark to each assessment submission during marking. The mark will correspond to an appropriate letter grade when relevantly weighted. The final mark for the unit will be calculated by combining the marks for all assessment tasks according to the percentage weightings shown in the assessment summary.

Assessment Tasks

Name	Weighting	Hurdle	Due
Quiz 1	10%	No	Week 4
Assignment 1	20%	No	Week 7
Quiz 2	10%	No	Week 9
Assignment 2	20%	No	Week 11
Final Examination	40%	No	TBA

Quiz 1

Assessment Type ¹: Quiz/Test
Indicative Time on Task ²: 7.5 hours
Due: Week 4
Weighting: 10%

This test will give early feedback on students' understanding of basic AI concepts (in particular search) and Python programming skills, and help them prepare for Assignment 1.