Outline

Overview

An advanced alternative to COMP3308; covers material at an advanced and challenging level.

Unit details and rules

Academic unit	Computer Science
Credit points	6
Prerequisites ?	(INFO1110 or INFO1910 or ENGG1810 or DATA1002 or DATA1902) and distinction-level results in at least one 2000-level COMP or MATH or SOFT unit
Corequisites ?	None
Prohibitions ?	COMP3308
Assumed knowledge ?	Data structures and algorithms as covered in COMP2123 or COMP2823.
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Irena Koprinska, irena.koprinska@sydney.edu.au

Assessment

The census date for this unit availability is 31 March 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Final Exam Exam. A minimum of 40% on the exam is required to pass the course.	60%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Experimental design	Assignment 1 Computer program	11%	Week 07 Due date: 13 Apr 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Data analysis	Assignment 2 Computer program and report	24%	Week 11 Due date: 15 May 2026 at 23:59	n/a	AI allowed
Outcomes assessed:
Out-of-class quiz	Weekly homeworks Homework exercises	5%	Weekly	n/a	AI allowed
Outcomes assessed:
= hurdle task ? = group assignment ?

Assessment summary

Weekly homeworks – weekly homework exercises submitted online
Assignment 1 – writing a computer program to solve a given task using AI algorithms
Assignment 2 – writing a computer program to solve a given task using AI algorithms and a report discussing the results
Exam – supervised exam at the end of the semester
Exam requirement: A minimum of 40% on the exam is required to pass this course.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100
Distinction	75 - 84
Credit	65 - 74
Pass	50 - 64
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

Weekly homeworks – no late submissions are allowed; Assignment 1 and Assignment 2 - late submissions are allowed up to 3 days late. A penalty of 5% per day late will apply.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	1. Course overview; 2. Introduction to AI, history and state-of-the-art	Lecture (2 hr)
Week 02	1. Problem solving and search. Uninformed search; 2. Informed search 1	Lecture (2 hr)
Week 02	1. Problem solving and search. Uninformed search; 2. Informed search 1	Tutorial (1 hr)
Week 03	1. Informed search 2; 2. Local search	Lecture (2 hr)
Week 03	1. Informed search 2; 2. Local search	Tutorial (1 hr)
Week 04	Game playing	Lecture (2 hr)
Week 04	Game playing	Tutorial (1 hr)
Week 05	1. Introduction to machine learning; 2. Instance-based and rule-based methods	Lecture (2 hr)
Week 05	1. Introduction to machine learning; 2. Instance-based and rule-based methods	Tutorial (1 hr)
Week 06	1. Statistical-based learning; 2. Evaluating and comparing classifiers	Lecture (2 hr)
Week 06	1. Statistical-based learning; 2. Evaluating and comparing classifiers	Tutorial (1 hr)
Week 07	Decision trees	Lecture (2 hr)
Week 07	Decision trees	Tutorial (1 hr)
Week 08	1. Introduction to neural networks; 2. Perceptrons; 3. Multilayer neural networks 1	Lecture (2 hr)
Week 08	1. Introduction to neural networks; 2. Perceptrons; 3. Multilayer neural networks 1	Tutorial (1 hr)
Week 09	1. Multilayer neural networks; 2. Deep learning	Lecture (2 hr)
Week 09	1. Multilayer neural networks; 2. Deep learning	Tutorial (1 hr)
Week 10	1. Support vector machines; 2. Ensembles of classifiers	Lecture (2 hr)
Week 10	1. Support vector machines; 2. Ensembles of classifiers	Tutorial (1 hr)
Week 11	1. Probabilistic reasoning; 2. Bayesian networks and inference in them	Lecture (2 hr)
Week 11	1. Probabilistic reasoning; 2. Bayesian networks and inference in them	Tutorial (1 hr)
Week 12	Unsupervised learning	Lecture (2 hr)
Week 12	Unsupervised learning	Tutorial (1 hr)
Week 13	1. Applications of AI; 2. Revision and preparation for the exam	Lecture (2 hr)
Week 13	1. Applications of AI; 2. Revision and preparation for the exam	Tutorial (1 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

Textbook:

Stuart J. Russell and Peter Norvig, Artificial Intelligence - A Modern Approach (Fourth edition). Pearson, 2021

Recommended book:

Ian H. Witten, I. Eibe Frank, Mark A. Hall and Christopher J. Pal, Data Mining: Practical Machine Learning Tools and Techniques (Fourth edition). Morgan Kaufmann, 2017.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Formulate problem space description, select and apply suitable search algorithms and analyse the issues involved
LO2. Understand and apply minimax search and alpha-beta pruning in game playing
LO3. Understand the basic principles and analyse the strengths, weaknesses and applicability of some of the main AI algorithms for supervised learning, unsupervised learning and probabilistic reasoning
LO4. Gain practical experience in designing, implementing and evaluating AI algorithms
LO5. Present and interpret data and information in verbal and written form
LO6. Appreciate some of the main ideas and views in AI, achievements and shortcomings of AI and the links between AI and other Computer Science areas

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

No major changes, only updates and small adjustments in assessment weights - positive student feedback.

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links

COMP3608: Introduction to Artificial Intelligence (Adv)

Semester 1, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Disclaimer

On this page

Useful links