Knowledge Technologies (COMP30018)
Undergraduate level 3Points: 12.5Not available in 2017
About this subject
Overview
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AIMS
Much of the world's knowledge is stored in the form of unstructured data (e.g. text) or implicitly in structured data (e.g. relational databases). In this subject students will learn algorithms and data structures for extracting, retrieving and storing analysing explicit knowledge from various data sources, with a focus on the web.
The aim of this subject is to introduce students to knowledge technologies and to provide a foundational knowledge of data science. The subject will also give students exposure to what applied research is all about.
INDICATIVE CONTENT
Topics include: data encoding and markup, web crawling, regular expressions, document indexing, text retrieval, basic probability, clustering, pattern mining, Bayesian learning, instance-based learning, and prediction and approaches to evaluation of knowledge technologies.
Examples of projects that students may complete are:
- A method for automatically predicting the geo-location of a Twitter user on the basis of their posts
- An automatic method for tagging multilingual Wikipedia documents with Wikipedia categories
- A search engine for Twitter data, which takes into account the time stamp of the query and documents
- A search engine for web user forum data
- A search engine servicing mixed monolingual queries (as in monolingual queries from a range of languages) over a large-scale document collection
- Classification and prediction of some real world problems using machine learning techniques.
Intended learning outcomes
INTENDED LEARNING OUTCOMES (ILO)
Having completed this unit the student is expected to describe and apply the fundamentals of knowledge systems, including data acquisition and aggregation, knowledge extraction, text retrieval, machine learning and data mining.
On completion of this subject the student is expected to:
- Gain an understanding of a representative selection of knowledge technology techniques in both theoretical and applied contexts
- Develop familiarity with component technologies used in commonly-deployed knowledge technology systems
- Get a feel for what research is all about, especially relating to knowledge technology-related projects underway at The University of Melbourne.
Generic skills
On completion of this subject, students should have developed the following skills:
- An ability to apply knowledge of basic science and engineering fundamentals
- An ability to undertake problem identification, formulation and solution
- The capacity to solve problems, including the collection and evaluation of information
- The capacity for critical and independent thought and reflection
- An expectation of the need to undertake lifelong learning, and the capacity to do so.
Last updated: 14 March 2025
Eligibility and requirements
Prerequisites
One of the following:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP20003 | Algorithms and Data Structures | Semester 2 (On Campus - Parkville) |
12.5 |
| COMP90038 | Algorithms and Complexity |
Semester 2 (On Campus - Parkville)
Semester 1 (On Campus - Parkville)
|
12.5 |
| COMP20007 | Design of Algorithms | Semester 1 (On Campus - Parkville) |
12.5 |
| ENGR30003 | Numerical Programming for Engineers | Semester 2 (On Campus - Parkville) |
12.5 |
Corequisites
None
Non-allowed subjects
Students cannot enrol in and gain credit for this subject and:
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP90049 | Knowledge Technologies |
Semester 1 (On Campus - Parkville)
Semester 2 (On Campus - Parkville)
|
12.5 |
433-352 Data on the Web
Inherent requirements (core participation requirements)
The University of Melbourne is committed to providing students with reasonable adjustments to assessment and participation under the Disability Standards for Education (2005), and the Assessment and Results Policy (MPF1326). Students are expected to meet the core participation requirements for their course. These can be viewed under Entry and Participation Requirements for the course outlines in the Handbook.
Further details on how to seek academic adjustments can be found on the Student Equity and Disability Support website: http://services.unimelb.edu.au/student-equity/home
Last updated: 14 March 2025
Assessment
Additional details
- Project work during semester incorporating both programming and a report, requiring approximately 30 - 35 hours of work; one project due approximately mid-semester, and a second due in Week 11 or 12 (30%)
- A mid-semester test (10%)
- 2-hour end-of-semester examination (60%).
Hurdle Requirement: To pass the subject, students must obtain at least:
- 15/30 in project work
- And 35/70 in the mid-semester test and end-of-semester written examination combined.
ILO 1 is addressed in the projects (applied) and the mid-semester test and final exam (theoretical). ILO 2 is addressed in the projects (through using a range of systems that are provided to students or that students experiment with themselves). ILO 3 is also addressed in the projects (which are generally themed around projects underway at the University, to give them a more applied feel).
Last updated: 14 March 2025
Dates & times
Not available in 2017
Time commitment details
170 hours
Last updated: 14 March 2025
Further information
- Texts
- Subject notes
LEARNING AND TEACHING METHODS
The subject is delivered through a combination of lectures and tutorials. One feature of the subject is that the projects are designed to be relatively open-ended and broad enough that students have scope to get hands-on experience implementing the breadth of material covered in the subject, as well as building off the subject content in innovating their own methods/ researching related methods from the research literature and implementing them such methods themselves.
INDICATIVE KEY LEARNING RESOURCES
Students will have access to lecture slides, readings relating to the lecture materials (both from a textbook and conference/journal papers), tutorial worksheets with worked solutions for all numeric problems, and sample reports to use in writing the project reports. Students are permitted to do their programming in any language and any programming environment/OS, and may be given the option of working in a team (with suitably increased expectations on what they are required to do). In recent years, the projects have been hosted on Kaggle, supporting a live “scoreboard” for student systems, and giving the projects more of a real-world feel.
CAREERS / INDUSTRY LINKS
The knowledge technologies industry (encompassing machine learning, data science, natural language processing and information retrieval) has been growing rapidly over the past two decades, with key industry players including Google, Microsoft, Amazon, Facebook and Twitter. Google has sponsored a prize for the highest-achieving student in the subject each in recent years, underlining its interest in the subject material. There have been guest lecturers in the subject from organisations including Palantir Technologies and NICTA.
- Related Handbook entries
This subject contributes to the following:
Type Name Informal specialisation Master of Engineering (Software with Business) Specialisation (formal) Software with Business Informal specialisation Science-credited subjects - new generation B-SCI and B-ENG. Informal specialisation Selective subjects for B-BMED - Breadth options
- Available through the Community Access Program
About the Community Access Program (CAP)
This subject is available through the Community Access Program (also called Single Subject Studies) which allows you to enrol in single subjects offered by the University of Melbourne, without the commitment required to complete a whole degree.
Please note Single Subject Studies via Community Access Program is not available to student visa holders or applicants
Entry requirements including prerequisites may apply. Please refer to the CAP applications page for further information.
Additional information for this subject
Subject coordinator approval required
- Available to Study Abroad and/or Study Exchange Students
Last updated: 14 March 2025