Course coordinator
Dr Alina Bialkowski
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Undergraduate
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
Methods and techniques within the field of artificial intelligence, including topics on search, reasoning and planning with certainty, decision-making under uncertainty, learning to act and reasoning about other agents. Specific emphasis on the practical utility of algorithms and their implementation in software.
This course provides foundational concepts, methods and techniques usedﾠin artificial intelligence - both theoretical and practical. The course aims to provide the student with aﾠbroad understanding of the field of artificial intelligence and introduces several of the most important algorithms and techniques that have found theoretical and practical applicability when constructing intelligent agents. The course includes topics on search, reasoning and planning with certainty,ﾠreasoning and planning under uncertainty, learning to act and reasoning about other agents.ﾠThe course enables the student to:
- gain an appreciation for the scientific context of artificial intelligence.
- understand and develop computing algorithms based on the principles of artificial intelligence, and to analyse their properties.
- find the right tools for solving specific problems, and to implement such tools in software.
This year's changes include the addition of more code demonstrations in lectures, a greater focus on reinforcement learning (and deep reinforcement learning), and the introduction of interviews to test assignment code understanding for a subset of students.
Course requirements
Assumed background
The course assumes students have the ability to create computer programs using a high-level programming language (esp. Python). Students should have some knowledge of abstract data structures, in particular tree and graph data structures, understand their use in programs, and be able to implement them using a high-level programming language.ﾠThe course also assumes students haveﾠbasic knowledge of set theory, logic,ﾠprobability andﾠcomputational complexityﾠ(e.g.ﾠbig-O notation and how to compute big-O complexity for simple algorithms).
Prerequisites
You'll need to complete the following courses before enrolling in this one:
(CSSE1001 or CSSE7030) or ENGG1001
Recommended prerequisites
We recommend completing the following courses before enrolling in this one:
MATH1061
Incompatible
You can't enrol in this course if you've already completed the following:
COMP3701 or COMP7701 or COMP7702
Course contact
Course staff
Lecturer
Dr Alina Bialkowski
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The courseﾠaims to introduce the foundational concepts and methods used in the field of artificial intelligence and provide students with skills to apply these techniques. Specifically the course aims to give students an overview of the following topics in artificial intelligence:
- searching for solutions to problems,
- reasoning and planning with certainty,
- reasoning and planning under uncertainty,
- learning to act, and
- reasoning about other agents.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Describe the core theoretical and conceptual frameworks, methods and practices which form the basis of artificial intelligence.
LO2.
Explain the properties and functions of a range of different artificial intelligence methods and to be able to connect a method to appropriate theoretical foundations.
LO3.
Effectively solve problems relating to artificial intelligence topics and applications discussed in class and in the literature.
LO4.
Implement techniques and methods from artificial intelligence using a high-level programming language.
LO5.
Effectively formulate real-world problems as problem representations solvable by existing techniques in artificial intelligence.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Computer Code, Paper/ Report/ Annotation, Project |
Assignment 1
|
20% |
23/08/2024 1:00 pm |
| Computer Code, Paper/ Report/ Annotation, Project |
Assignment 2
|
20% |
20/09/2024 1:00 pm |
| Computer Code, Paper/ Report/ Annotation, Project |
Assignment 3
|
20% |
25/10/2024 1:00 pm |
| Examination |
Final Exam
|
40% |
End of Semester Exam Period 2/11/2024 - 16/11/2024 |
A hurdle is an assessment requirement that must be satisfied in order to receive a specific grade for the course. Check the assessment details for more information about hurdle requirements.
Assessment details
Assignment 1
- Online
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
23/08/2024 1:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The assignments will comprise three components to complete during the semester, with assessable items due in weeks 5, 9 and 13. Assignment submission will consist of code and an associated report; detailed specifications will be provided on Blackboard at the required time.
The assignments are designed to test a student's:
- understanding of artificial intelligence techniques.
- practical application of skills acquired in contact sessions using high-level programming language.
- ability in choosing an appropriate artificial intelligence technique to solve a problem; and
- ability to convey their solution in a manner accessible by their peers.
Please ensure that you download and begin your assignments as soon as possible on or after the date of issue. Students are advised to create back-up copies of their assignments, as software can fail (or be lost) for many reasons and it happens quite frequently; it is recommended that you make use of a software version control system (e.g. git), with appropriate privacy settings in place to prevent inadvertently violating academic integrity standards.
Submission guidelines
Assignments will be submitted via Gradescope (details will be provided on Blackboard).
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback will be released to students within approximately 14 days of submission.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 2
- Online
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
20/09/2024 1:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The assignments will comprise three components to complete during the semester, with assessable items due in weeks 5, 9 and 13. Assignment submission will consist of code and an associated report; detailed specifications will be provided on Blackboard at the required time.
The assignments are designed to test a student's:
- understanding of artificial intelligence techniques.
- practical application of skills acquired in contact sessions using high-level programming language.
- ability in choosing an appropriate artificial intelligence technique to solve a problem; and
- ability to convey their solution in a manner accessible by their peers.
Please ensure that you download and begin your assignments as soon as possible on or after the date of issue. Students are advised to create back-up copies of their assignments, as software can fail (or be lost) for many reasons and it happens quite frequently; it is recommended that you make use of a software version control system (e.g. git), with appropriate privacy settings in place to prevent inadvertently violating academic integrity standards.
Submission guidelines
Assignments will be submitted via Gradescope (details will be provided on Blackboard).
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback will be released to students within approximately 14 days of submission.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Assignment 3
- Online
- Mode
- Written
- Category
- Computer Code, Paper/ Report/ Annotation, Project
- Weight
- 20%
- Due date
25/10/2024 1:00 pm
- Learning outcomes
- L01, L02, L03, L04, L05
Task description
The assignments will comprise three components to complete during the semester, with assessable items due in weeks 5, 9 and 13. Assignment submission will consist of code and an associated report; detailed specifications will be provided on Blackboard at the required time.
The assignments are designed to test a student's:
- understanding of artificial intelligence techniques.
- practical application of skills acquired in contact sessions using high-level programming language.
- ability in choosing an appropriate artificial intelligence technique to solve a problem; and
- ability to convey their solution in a manner accessible by their peers.
Please ensure that you download and begin your assignments as soon as possible on or after the date of issue. Students are advised to create back-up copies of their assignments, as software can fail (or be lost) for many reasons and it happens quite frequently; it is recommended that you make use of a software version control system (e.g. git), with appropriate privacy settings in place to prevent inadvertently violating academic integrity standards.
Submission guidelines
Assignments will be submitted via Gradescope (details will be provided on Blackboard).
Deferral or extension
You may be able to apply for an extension.
The maximum extension allowed is 7 days. Extensions are given in multiples of 24 hours.
Marked assignments with feedback will be released to students within approximately 14 days of submission.
Late submission
A penalty of 10% of the maximum possible mark will be deducted per 24 hours from time submission is due for up to 7 days. After 7 days, you will receive a mark of 0.
Final Exam
- Hurdle
- Identity Verified
- Mode
- Written
- Category
- Examination
- Weight
- 40%
- Due date
End of Semester Exam Period
2/11/2024 - 16/11/2024
- Other conditions
- Time limited.
- Learning outcomes
- L01, L02, L03, L05
Task description
The end of semester exam will examine all of the material covered during the semester. This exam will be closed-book and may contain multiple choice, short-answer, essay and problem-solving questions.
Hurdle requirements
In order to achieve a grade of 4 or better, you must obtain a minimum mark of 50% on the final exam. If you do not obtain at least 50% on the final exam, then your overall mark is capped at 49% and your final grade is capped at a 3.Exam details
| Planning time | 10 minutes |
|---|---|
| Duration | 120 minutes |
| Calculator options | (In person) Casio FX82 series only or UQ approved and labelled calculator |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Paper based |
| Invigilation | Invigilated in person |
Submission guidelines
Deferral or extension
You may be able to defer this exam.
Course grading
Full criteria for each grade is available in the Assessment Procedure.
| Grade | Cut off Percent | Description |
|---|---|---|
| 1 (Low Fail) | 0 - 19 |
Absence of evidence of achievement of course learning outcomes. |
| 2 (Fail) | 20 - 44 |
Minimal evidence of achievement of course learning outcomes. |
| 3 (Marginal Fail) | 45 - 49 |
Demonstrated evidence of developing achievement of course learning outcomes |
| 4 (Pass) | 50 - 64 |
Demonstrated evidence of functional achievement of course learning outcomes. |
| 5 (Credit) | 65 - 74 |
Demonstrated evidence of proficient achievement of course learning outcomes. |
| 6 (Distinction) | 75 - 84 |
Demonstrated evidence of advanced achievement of course learning outcomes. |
| 7 (High Distinction) | 85 - 100 |
Demonstrated evidence of exceptional achievement of course learning outcomes. |
Additional course grading information
In order to achieve a grade of 4 or better, you must obtain a minimum mark of 50% on the final exam. If you do not obtain at least 50% on the final exam, then your overall mark is capped at 49% and your final grade is capped at a 3.
Standard algebraic rounding (e.g., 84.5 rounds to an 85, whereas 84.4 rounds to an 84) will be applied to the final mark prior to allocation of final grade. This only applies to the final mark and course grade, not to individual pieces of assessment; any fractional results are included, as is, in the calculation of the final mark.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
Artificial Intelligence (AI)
Note that all assignments are to be worked on individually and must be your own work except where the use of code written or provided by other entities (teaching staff, Linux man pages, AI tools, etc.) is explicitly permitted by the assignment specification. Artificial Intelligence (AI) tools are permitted to be used in this course, but they are not required to be used and not recommended to be used as they may inhibit learning and introduce bugs into your code. You must always follow the referencing requirements set out in the assignment specification and documents referenced from the assignment specification. Failure to appropriately reference the resources (tools and information sources) used in your work may result in misconduct allegations against you. You must clearly reference any use of AI in each instance. You are encouraged to discuss the concepts behind the assignments but under no circumstances should you show your code to, or allow your code to be seen by, another student. You should not look at the code of any other student. You must sufficiently protect all electronic and paper copies of your code. All submitted code will be subject to electronic plagiarism and collusion detection. Assignments with no academic merit will be awarded a mark of zero. You may have to verbally answer questions about your submission as part of the assignment marking process.ﾠ
In accordance with the Assessment Procedure, marks may be moderated, and grade cutoffs may be lowered if academically justified.
Programming Assignment Interviews
For the programming component of the assignments, the teaching staff will conduct interviews with a subset of students about their submissions for the purpose of establishing genuine authorship. If you write your own code, you have nothing to fear from this process. If you legitimately use permitted code from other sources (following the usage/referencing requirements in the assignment specification, then you are expected to understand that code. If you are not able to adequately explain the design of your solution and/or adequately explain your submitted code (and/or earlier versions in your repository) and/or be able to make simple modifications to your solution as requested at the interview, then your assignment mark will be scaled down based on the level of understanding you are able to demonstrate and/or your submission may be subject to a misconduct investigation where your interview responses form part of the evidence. Interview invitations will be issued by email to your student email account at any time up until the end of week one of the exam period. Failure to respond to an interview invitation by the deadline stated in the invitation (which will be at least one week after the invitation is sent) or failure to attend a scheduled interview will result in zero marks for the assignment unless exceptional circumstances can be demonstrated with supporting evidence.
Having Troubles?
If you are having difficulties with any aspect of the course material, you should seek help. Speak to the course teaching staff.
If external circumstances are affecting your ability to work on the course, you should seek help as soon as possible. The University and UQ Union have organisations and staff who are able to help, for example, UQ Student Services are able to help with study and exam skills, tertiary learning skills, writing skills, financial assistance, personal issues, and disability services (among other things).
Complaints and criticisms should be directed in the first instance to the course coordinator. If you are not satisfied with the outcome, you may bring the matter to the attention of the School of EECS Director of Teaching and Learning.
Learning resources
You'll need the following resources to successfully complete the course. We've indicated below if you need a personal copy of the reading materials or your own item.
Library resources
Find the required and recommended resources for this course on the UQ Library website.
Additional learning resources information
Additional resources from the internet will be provided along the way, as the need arises.
Learning activities
The learning activities for this course are outlined below. Learn more about the learning outcomes that apply to this course.
Filter activity type by
Please select
| Learning period | Activity type | Topic |
|---|---|---|
Multiple weeks |
Problem-based learning |
In-class exercises A series of exercises will be conducted in lecture to provide opportunity for better student engagement with content and in-depth discussions. Learning outcomes: L01, L02, L03, L05 |
Lecture |
Lectures Learning outcomes: L01, L02, L03, L04, L05 |
|
Practical |
Practicals/Theory Class Tutorials will provide opportunity to practice the techniques introduced in lectures Learning outcomes: L01, L02, L03, L04, L05 |
|
Information technology session |
RiPPLE personalised learning activities Students will be required to complete four RiPPLE rounds, which includes answering, generating and reviewing instructor and peer-generated questions. Learning outcomes: L01, L02, L03, L05 |
Policies and procedures
University policies and procedures apply to all aspects of student life. As a UQ student, you must comply with University-wide and program-specific requirements, including the:
- Student Code of Conduct Policy
- Student Integrity and Misconduct Policy and Procedure
- Assessment Procedure
- Examinations Procedure
- Reasonable Adjustments - Students Policy and Procedure
Learn more about UQ policies on my.UQ and the Policy and Procedure Library.
School guidelines
Your school has additional guidelines you'll need to follow for this course: