Course coordinator
Professor Guido Zuccon
Course overview
- Study period
- Semester 2, 2024 (22/07/2024 - 18/11/2024)
- Study level
- Postgraduate Coursework
- Location
- St Lucia
- Attendance mode
- In Person
- Units
- 2
- Administrative campus
- St Lucia
- Coordinating unit
- Elec Engineering & Comp Science School
The course discusses the theory, design, and implementation of Information Retrieval (IR) techniques in text-based information systems. The theoretical component of the course focuses on IR methods for the processing, indexing, querying, ranking, organisation, and classification of textual documents, including Web documents. A variety of current research topics are also covered, including professional search and recommendation systems. The practical component of the course addresses the design and implementation of high-capacity text retrieval and filtering systems such as web search engines.
The ubiquity of text-based information systems (e.g., Web search engines, online advertising, and recommender systems) is fuelling the rapid growth of interest in studying various topics in the area of Information Retrieval (IR). The remarkable success of search engines like Google, Bing and the likesﾠis a striking example of how important it is for academia and industry to foster innovation in the field of IR and text-based information systems. This course will coverﾠvarious IR techniques that are employed into building a complete text-based information system. Changes to assessment have been made with weekly quizzes replacing mid-semester exam to encourage students to engage throughout the course. Introduced a new module focused on generative large language models and generative IR due to their importance in fuelling the latest developments in the field.
Course requirements
Assumed background
Background in data managements (e.g.,ﾠSQL and indexing),ﾠprobability and statistics, algorithms and data structures;ﾠsolid programming skillsﾠ(Python) and algorithmical thinking.
Prerequisites
You'll need to complete the following courses before enrolling in this one:
INFS2200 or INFS7903
Course contact
Course staff
Lecturer
Professor Guido Zuccon
Timetable
The timetable for this course is available on the UQ Public Timetable.
Aims and outcomes
The aim of this course is to provide a comprehensive introduction to Information Retrieval (IR). The areas covered include data acquisition andﾠpre-processing (crawling, stemming), indexing, querying, ranking, organisation and representation of textual documents, evaluation. The goal is to present fundamental concepts and algorithms for each topic, thus providing the students with the necessary background and practical skills for the application of information retrieval in web search engines. In addition, this course also provides a starting point for those students who are interested in pursuing research in information retrieval or related fields.
Learning outcomes
After successfully completing this course you should be able to:
LO1.
Gain in-depth knowledge of the core principles in the information retrieval field such as text representation and similarity computation, indexing textual documents, query correction and expansion, retrieval models, performance evaluation and metrics
LO2.
Acquire hands-on practical experience with the development of components of Web search engines and experimentation of end-to-end systems using relevant open-source libraries.
LO3.
Understand advanced topics in information retrieval research such as learning to rank, neural retrieval and ranking.
LO4.
Create, analyse and evaluate novel information retrieval solutions to search related problems
LO5.
Compare and contrast information retrieval methods and communicate their differences, advantages and disadvantages, based on quantitative evaluation.
LO6.
Analyse search tasks and problems, and identify and communicate relevant information retrieval solutions.
Assessment
Assessment summary
| Category | Assessment task | Weight | Due date |
|---|---|---|---|
| Quiz |
Weekly quizzes
|
10% |
26/07/2024 3:00 pm 2/08/2024 3:00 pm 9/08/2024 3:00 pm 16/08/2024 3:00 pm 23/08/2024 3:00 pm 30/08/2024 3:00 pm 6/09/2024 3:00 pm 13/09/2024 3:00 pm 20/09/2024 3:00 pm 4/10/2024 3:00 pm 11/10/2024 3:00 pm 18/10/2024 3:00 pm 25/10/2024 3:00 pm |
| Computer Code, Project | Project Part 1 | 20% |
30/08/2024 4:00 pm |
| Computer Code, Project | Project Part 2 | 20% |
18/10/2024 4:00 pm |
| Examination |
Final Oral Exam
|
50% |
4/11/2024 - 15/11/2024 |
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
Weekly quizzes
- Online
- Mode
- Written
- Category
- Quiz
- Weight
- 10%
- Due date
26/07/2024 3:00 pm
2/08/2024 3:00 pm
9/08/2024 3:00 pm
16/08/2024 3:00 pm
23/08/2024 3:00 pm
30/08/2024 3:00 pm
6/09/2024 3:00 pm
13/09/2024 3:00 pm
20/09/2024 3:00 pm
4/10/2024 3:00 pm
11/10/2024 3:00 pm
18/10/2024 3:00 pm
25/10/2024 3:00 pm
- Learning outcomes
- L01, L03
Task description
Online Quizzes on Blackboard. To be completed individually.
Submission guidelines
Deferral or extension
You cannot defer or apply for an extension for this assessment.
To accommodate unforeseen circumstances such as illness, your quiz score will be based on the best 10 out of 13 submissions.
Late submission
100% Late Penalty after 1 hour grace period. The one-hour grace period is recorded from the time the submission is due.
Project Part 1
- Mode
- Written
- Category
- Computer Code, Project
- Weight
- 20%
- Due date
30/08/2024 4:00 pm
- Learning outcomes
- L02, L04, L05, L06
Task description
This project requires students to implement a set of identified indexing and retrieval methods. The students also need to evaluate and analyse the results of the techniques.
This is an individually assessed project.
Submission guidelines
UQ Blackboard Online Submission
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 and/or detailed solutions with feedback will be released to students within 14-21 days, where the earlier time frame applies if there are no extensions.
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.
Project Part 2
- Mode
- Written
- Category
- Computer Code, Project
- Weight
- 20%
- Due date
18/10/2024 4:00 pm
- Learning outcomes
- L02, L04, L05, L06
Task description
This project requires students to implement a set of identified indexing and retrieval methods. The students also need to evaluate and analyse the results of the techniques.
This is an individually assessed project.
Submission guidelines
UQ Blackboard Online Submission
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 and/or detailed solutions with feedback will be released to students within 14-21 days, where the earlier time frame applies if there are no extensions.
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 Oral Exam
- Hurdle
- Identity Verified
- In-person
- Mode
- Oral
- Category
- Examination
- Weight
- 50%
- Due date
4/11/2024 - 15/11/2024
- Learning outcomes
- L01, L03, L05, L06
Task description
All topics covered.
Hurdle requirements
To pass this course, a minimum of 50% must be obtained in the final exam.Exam details
| Planning time | no planning time minutes |
|---|---|
| Duration | 60 minutes |
| Calculator options | No calculators permitted |
| Open/closed book | Closed Book examination - no written materials permitted |
| Exam platform | Other |
| 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
Note that you have to score at least 25 out of 50 marks (50%) for the final exam to pass the course (otherwise the mark will be capped at 3), and percentages will not be roundedﾠbefore any grade cut-offs apply.
Supplementary assessment
Supplementary assessment is available for this course.
Additional assessment information
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.
Generative AI and Machine Translation in Assessment
- Weekly Quizzes: This assessment task evaluates students' abilities, skills and knowledge without the aid of generative Artificial Intelligence (AI) or Machine Translation (MT). Students are advised that the use of AI technologies to develop responses is strictly prohibited and may constitute student misconduct under the Student Code of Conduct.
- Project (Part 1 and Part 2): Artificial Intelligence (AI) and Machine Translation (MT) are emerging tools that may support students in completing this assessment task. Students may appropriately use AI and/or MT in completing this assessment task. Students must clearly reference any use of AI or MT in each instance. A failure to reference generative AI or MT use may constitute student misconduct under the Student Code of Conduct.
- Oral Examination: This assessment task is to be completed in-person. The use of generative Artificial Intelligence (AI) and Machine Translation (MT) tools will not be permitted. Any attempted use of Generative AI may constitute student misconduct under the Student Code of Conduct.
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
Course material will be published on the course website
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 From Week 1 To Week 13 |
Practical |
Practicals Practicals will provide an opportunity to further understand the concepts and techniques introduced in the lectures via examples, exercises, projects and problem-solving. Further concepts will be developed during these activities. There will be a mix of theory and hands-on activities. NOTE: these sessions start from week 1 and run until week 13 included. Learning outcomes: L02, L04, L05, L06 |
Week 1 (22 Jul - 28 Jul) |
Lecture |
Intro, Search Engine Architecture, Text Analysis Introduction, Motivation and Logistic. Architecture of a Search Engine, key components of study across this course. Zipf's Law, Stemming, Stopwording. Indexing techniques and processing, including data structures. Learning outcomes: L01, L04, L05 |
Week 2 (29 Jul - 04 Aug) |
Lecture |
Offline Evaluation Offline evaluation methodology and evaluation frameworks, set-based and rank-based evaluation measures, statistical significance testing, parameter tuning. Learning outcomes: L01, L05 |
Week 3 (05 Aug - 11 Aug) |
Lecture |
Retrieval Models 1 Retrieval Models: term-based and term dependency methods for matching and ranking documents. Learning outcomes: L01, L05 |
Week 4 (12 Aug - 18 Aug) |
Lecture |
Retrieval Models 2 Fusion methods and semantic matching methods Learning outcomes: L01, L05 |
Week 5 (19 Aug - 25 Aug) |
Lecture |
Retrieval Models 3 Methods based on query analysis, query expansion, word embeddings, relevance feedback Learning outcomes: L01, L05 |
Week 6 (26 Aug - 01 Sep) |
Lecture |
Retrieval Models 4 Learning to rank Learning outcomes: L03, L05 |
Week 7 (02 Sep - 08 Sep) |
Lecture |
Retrieval Models 5 Exploit Implicit Signals: Online Learning to rank, federated online learning to rank, counterfactual learning, online evaluation, click models Learning outcomes: L01, L03, L05 |
Week 8 (09 Sep - 15 Sep) |
Lecture |
Retrieval Models 6 Pre-trained language models, Transformers and the BERT rankers Learning outcomes: L03, L05 |
Week 9 (16 Sep - 22 Sep) |
Lecture |
Retrieval Models 7 Dense Retrievers Learning outcomes: L03, L05 |
Week 11 (07 Oct - 13 Oct) |
Lecture |
Index Compression Entropy and ambiguity, methods for compression: delta encoding, bit aligned codes, byte aligned codes; looking ahead and skipping Learning outcomes: L01, L05 |
Week 12 (14 Oct - 20 Oct) |
Lecture |
Crawling and Link Analysis Methods for crawling Web pages; making use of link information for retrieval: PageRank and HITS. Learning outcomes: L01, L05 |
Week 13 (21 Oct - 27 Oct) |
Lecture |
Interactive IR, Diversity Evaluation practices based on experiments with users. Ranking methods for diversity, ambiguity and redundancy Learning outcomes: L04, L05, L06 |
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: