1. Handbook
  2. Subjects
  3. Knowledge Technologies

Knowledge Technologies (COMP30018)

Undergraduate level 3Points: 12.5Not available in 2018

You’re viewing the 2018 Handbook:
Or view archived Handbooks
You’re currently viewing the 2018 version of this subject

Overview

Year of offerNot available in 2018
Subject levelUndergraduate Level 3
Subject codeCOMP30018
FeesSubject EFTSL, Level, Discipline & Census Date

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:

  1. Gain an understanding of a representative selection of knowledge technology techniques in both theoretical and applied contexts
  2. Develop familiarity with component technologies used in commonly-deployed knowledge technology systems
  3. 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 February 2019