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Unit of study_

COMP3608: Introduction to Artificial Intelligence (Adv)

Semester 1, 2022 [Normal day] - Remote

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

Unit details and rules

Unit code COMP3608
Academic unit Computer Science
Credit points 6
Prohibitions
? 
COMP3308
Prerequisites
? 
Distinction-level results in at least one 2000 level COMP or MATH or SOFT unit
Corequisites
? 
None
Assumed knowledge
? 

Algorithms. Programming skills (e.g. Java, Python, C, C++, Matlab)

Available to study abroad and exchange students

Yes

Teaching staff

Coordinator Irena Koprinska, irena.koprinska@sydney.edu.au
Type Description Weight Due Length
Final exam (Record+) Type B final exam Final exam
Online exam. A minimum of 40% on the exam is required to pass the course.
60% Formal exam period 2 hours
Outcomes assessed: LO1 LO2 LO3 LO5 LO6
Assignment Assignment 1
Computer program. See Canvas for more details.
12% Week 07 n/a
Outcomes assessed: LO2 LO4
Assignment group assignment Assignment 2
Computer program and report. Individual or in a group of 2 students.
24% Week 10 n/a
Outcomes assessed: LO3 LO4 LO5
Assignment Homeworks
Homework exercises. See Canvas for more details.
4% Weekly n/a
Outcomes assessed: LO1 LO2 LO3 LO5 LO6
group assignment = group assignment ?
Type B final exam = Type B final exam ?

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 – online 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 sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

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 Current Student website  provides information on academic integrity and the resources available to all students. The University expects students and staff to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.  

We use similarity detection software to detect potential instances of plagiarism or other forms of academic integrity breach. If such matches indicate evidence of plagiarism or other forms of academic integrity breaches, your teacher is required to report your work for further investigation.

You may only use artificial intelligence and writing assistance tools in assessment tasks if you are permitted to by your unit coordinator, and if you do use them, you must also acknowledge this in your work, either in a footnote or an acknowledgement section.

Studiosity is permitted for postgraduate units unless otherwise indicated by the unit coordinator. The use of this service must be acknowledged in your submission.

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.

WK Topic Learning activity Learning outcomes
Week 01 1. Course overview; 2. Introduction to AI, history and state-of-the-art Lecture and tutorial (2 hr) LO6
Week 02 1. Problem solving and search. Uninformed search; 2. Informed search 1 Lecture and tutorial (3 hr) LO1
Week 03 1. Informed search 2; 2. Local search Lecture and tutorial (3 hr) LO1
Week 04 Game playing Lecture and tutorial (3 hr) LO2
Week 05 1. Introduction to machine learning; 2. Instance-based and rule-based methods Lecture and tutorial (3 hr) LO3 LO6
Week 06 1. Statistical-based learning; 2. Evaluating and comparing classifiers Lecture and tutorial (3 hr) LO3
Week 07 Decision trees Lecture and tutorial (3 hr) LO3
Week 08 1. Introduction to neural networks; 2. Perceptrons; 3. Multilayer neural networks 1 Lecture and tutorial (3 hr) LO3
Week 09 1. Multilayer neural networks; 2. Deep learning Lecture and tutorial (3 hr) LO3
Week 10 1. Support vector machines; 2. Ensembles of classifiers Lecture and tutorial (3 hr) LO3
Week 11 1. Probabilistic reasoning; 2. Bayesian networks and inference in them Lecture and tutorial (3 hr) LO3
Week 12 Unsupervised learning Lecture and tutorial (3 hr) LO3
Week 13 1. Applications of AI; 2. Revision and preparation for the exam Lecture and tutorial (3 hr) LO2 LO3 LO6

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 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.

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
GQ1 GQ2 GQ3 GQ4 GQ5 GQ6 GQ7 GQ8 GQ9

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

No major changes, only updates - positive student feedback. Moving to a new assessment platform for the programming assignments due to new university policies.

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

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