Knowledge Technologies

Subject COMP90049 (2012)

Note: This is an archived Handbook entry from 2012.

Credit Points: 12.50
Level: 9 (Graduate/Postgraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2012:

Semester 1, Parkville - Taught on campus.
Pre-teaching Period Start not applicable
Teaching Period not applicable
Assessment Period End not applicable
Last date to Self-Enrol not applicable
Census Date not applicable
Last date to Withdraw without fail not applicable


Timetable can be viewed here. For information about these dates, click here.
Time Commitment: Contact Hours: 24 one-hour lectures (two per week) and 12 one-hour workshops (one per week)
Total Time Commitment:

120 hours
Prerequisites:

One of the following:

Subject
Study Period Commencement:
Credit Points:
Semester 1, Semester 2
12.50
Semester 1, Semester 2
12.50

OR

433-253 Algorithms and Data Structures
Corequisites:

None
Recommended Background Knowledge:

None
Non Allowed Subjects:
Subject

OR

433-352 Data on the Web
Core Participation Requirements:

For the purposes of considering request for Reasonable Adjustments under the Disability Standards for Education (Cwth 2005), and Students Experiencing Academic Disadvantage Policy, academic requirements for this subject are articulated in the Subject Description, Subject Objectives, Generic Skills and Assessment Requirements of this entry.The University is dedicated to provide support to those with special requirements. Further details on the Disability support scheme can be found at the Disability Liaison Unit Website:http://www.services.unimelb.edu.au/disability/

Coordinator

Prof Justin Zobel, Prof Rao Kotagiri

Contact

Dr Aaron Harwood

email: aharwood@unimelb.edu.au

Subject Overview:

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 storing explicit knowledge from various data sources, with a focus on the web. Topics include: data encoding and markup, web crawling, clustering, regular expressions, pattern mining, Bayesian learning, instance-based learning, document indexing, database storage and indexing, and text retrieval.
Objectives:

On successful completion of the subject, students should be able to:

  • Describe and apply the fundamentals of knowledge systems, including data acquisition and aggregation, knowledge extraction, text retrieval, machine learning and data mining
Assessment:
  • Project work during semester, expected to take about 36 hours (40%)
  • A mid-semester test (10%
  • And a 2-hour end-of-semester written examination (50%)

To pass the subject, students must obtain at least:

  • 50% overall
  • 20/40 in project work, and 30/60 in the mid-semester test and end-of-semester written examination combined
Prescribed Texts:

None
Breadth Options:

This subject is not available as a breadth subject.
Fees Information: Subject EFTSL, Level, Discipline & Census Date
Generic Skills:

On completion of this subject students should have developed the following generic skills:

  • Ability to undertake problem identification, formulation, and solution
  • Ability to utilise a systems approach to complex problems and to design and operational performance as well as an effective team member
  • Ability to manage information and documentation
  • Capacity for creativity and innovation
Related Course(s): Master of Engineering in Distributed Computing
Master of Information Technology
Master of Science (Computer Science)
Master of Software Systems Engineering
Related Majors/Minors/Specialisations: Computer Science
Master of Engineering (Software)

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