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Knowledge Technologies (COMP90049)
Graduate courseworkPoints: 12.5On Campus (Parkville)
About this subject
- Overview
- Eligibility and requirements
- Assessment
- Dates and times
- Further information
- Timetable(opens in new window)
Contact information
Semester 1
Semester 2
Dr Sarah Erfani
Overview
| Availability | Semester 1 Semester 2 |
|---|---|
| Fees | Look up fees |
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. databases). In this subject, students will learn algorithms and data structures for extracting, retrieving and analysing explicit knowledge from various data sources, with a focus on the web. Topics include: data encoding and mark-up, web crawling, regular expressions, document indexing, text retrieval, clustering, classification and prediction, pattern mining, and approaches to evaluation of knowledge technologies.
INDICATIVE CONTENT
Introduction to Knowledge Technologies; String search; Genomics; Text processing and search; Web search and retrieval; Introduction to Data Mining; Introduction to basic Probability; Classification; Association Rules; Clustering; Evaluation measures.
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
- Unification and resolution
- Nondeterminism, search, and backtracking
Intended learning outcomes
On completion of this subject the student is expected to:
- Apply the fundamentals of knowledge systems, including a representative selection of knowledge technology techniques in both theoretical and applied context
- Demonstrate practical skills with component technologies used in commonly-deployed knowledge technology systems
- Formulate a research methodology, especially relating to knowledge technology-related projects underway at The University of Melbourne
Generic skills
On completion of this subject, students should have the following generic skills:
- An ability to undertake problem identification, formulation, and develop solutions
- Synthesize complex systems and processes from fundamental knowledge
- Apply systematic thinking to address complex problems
Last updated: 3 November 2022
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 |
OR
Admission into one of the following courses:
MC-ENG Master of Engineering (200 program only)
MC-IT Master of Information Technology, 100 or 150 pt program in Distributed Computing, Computing or Cyber Security
MC-IT Master of Information Technology, 100 pt program in Human-Computer Interaction
MC-SCICMP Master of Science (Computer Science)
Corequisites
None
Non-allowed subjects
| Code | Name | Teaching period | Credit Points |
|---|---|---|---|
| COMP30018 | Knowledge Technologies | No longer available |
OR
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: 3 November 2022
Assessment
Additional details
- Project work requiring approximately 50-60 hours of work; one project due approximately mid-semester, and a second due in Week 11 or 12 (40%)
- One 2-hour written, closed-book examination held during the examination period (60%).
Hurdle requirement: To pass the subject, students must obtain at least:
- 50% overall
- 20/40 in project work, and 30/60 at the end-of-semester written examination combined.
Intended Learning Outcomes (ILO) 1 is addressed in in the 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: 3 November 2022
Dates & times
- Semester 1
Principal coordinator Rao Kotagiri Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week Total time commitment 200 hours Teaching period 4 March 2019 to 2 June 2019 Last self-enrol date 15 March 2019 Census date 31 March 2019 Last date to withdraw without fail 10 May 2019 Assessment period ends 28 June 2019 Semester 1 contact information
- Semester 2
Coordinator Sarah Monazam Erfani Mode of delivery On Campus (Parkville) Contact hours 36 hours, comprising of two 1-hour lectures and one 1-hour workshop per week Total time commitment 200 hours Teaching period 29 July 2019 to 27 October 2019 Last self-enrol date 9 August 2019 Census date 31 August 2019 Last date to withdraw without fail 27 September 2019 Assessment period ends 22 November 2019 Semester 2 contact information
Dr Sarah Erfani
Time commitment details
200 hours
Last updated: 3 November 2022
Further information
- Texts
Prescribed texts
None
- Subject notes
LEARNING AND TEACHING METHODS
This course is taught over 12 weeks, each week with two one hour formal lectures and a one hour workshop. During the workshops the students are given problems to solve to reinforce the previous week’s lecturing material. The problem solving nature of the workshops is geared for the students to learn and understand the concepts of the subject material.
INDICATIVE KEY LEARNING RESOURCESChristopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze (2008), Information Retrieval, Cambridge University Press. Freely available at informationretrieval.org
Pang-Ning Tan, Michael Steinbach and Vipin Kumar (2005) Introduction to Data Mining, Addison-Wesley.
CAREERS / INDUSTRY LINKS
This subject is relevant to many fields including Engineering, Commerce, Government Organizations, Research Institutes and Institutions in Medicine where data analysis can play a significant improvement in delivering services or improving profits.
- Related Handbook entries
This subject contributes to the following:
Type Name Course Master of Commerce (Decision, Risk and Financial Sciences) Course Doctor of Philosophy - Engineering Course Master of Philosophy - Engineering Course Master of Science (Computer Science) Course Ph.D.- Engineering Course Master of Data Science Specialisation (formal) Spatial Specialisation (formal) Distributed Computing Specialisation (formal) Computing Specialisation (formal) Software Specialisation (formal) Mechatronics Major Computer Science Specialisation (formal) Software with Business - 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.
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
Last updated: 3 November 2022