Knowledge Technologies

Subject COMP30018 (2012)

Note: This is an archived Handbook entry from 2012.

Credit Points: 12.50
Level: 3 (Undergraduate)
Dates & Locations:

This subject has the following teaching availabilities in 2012:

Semester 2, 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
Corequisites:

None

Recommended Background Knowledge:

None

Non Allowed Subjects:
Subject

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

Assoc Prof Tim Baldwin

Contact

Associate Professor Tim Baldwin

email: tbaldwin@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 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 (30%)
  • A mid-semester test (10%)
  • And a 2-hour end-of-semester written examination (60%)

To pass the subject, students must obtain at least:

  • 50% overall
  • 15/30 in project work
  • And 35/70 in the mid-semester test and end-of-semester written examination combined.
Prescribed Texts:

TBA

Breadth Options:

This subject potentially can be taken as a breadth subject component for the following courses:

You should visit learn more about breadth subjects and read the breadth requirements for your degree, and should discuss your choice with your student adviser, before deciding on your subjects.

Fees Information: Subject EFTSL, Level, Discipline & Census Date
Related Course(s): Bachelor of Engineering (Software Engineering)
Bachelor of Engineering (Software Engineering)/Bachelor of Science
Related Majors/Minors/Specialisations: B-ENG Software Engineering stream
Computer Science
Computer Science
Computing and Software Systems
Master of Engineering (Software)
Science credit subjects* for pre-2008 BSc, BASc and combined degree science courses
Science-credited subjects - new generation B-SCI and B-ENG. Core selective subjects for B-BMED.
Software Systems

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