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CECS Professional Skills Mapping

COMP4650 — Document Analysis

code: COMP4650
name: Document Analysis
unit value: 6
description: Processing of semi-structured documents such as internet pages, RSS feeds and their accompanying news items, and PDF brochures is considered from the perspective of interpreting the content. This course considers the \document" and its various genres as a fundamental object for business, government and community. For this, the course covers four broad areas: (A) information retrieval, (B) natural language processing, (C) machine learning for documents, and (D) relevant tools for the Web. Basic tasks here are covered including content collection and extraction, formal and informal natural language processing, information extraction, information retrieval, classification and analysis. Fundamental probabilistic techniques for performing these tasks, and some common software systems will be covered, though no area will be covered in any depth.
P&C: https://programsandcourses.anu.edu.au/course/COMP4650
course learning outcomes:
  1. Differentiate between the basic probabilistic theories of language and document structure, information retrieval, and classification, clustering and document feature engineering.
  2. Identify the basic algorithms and software available for probabilistic theories of language and be proficient at using common libraries for natural language processing to perform basic analysis tasks.
  3. Index a document collection for use in an information retrieval system. Demonstrate advanced knowledge of basic theories and algorithms to determine large scale named-entity matching and standardization of names within a collection.
  4. Perform automated classification using probabilistic theories.
  5. Have the background theory and practical knowledge necessary to plan and execute a basic document analysis project.
assessment:
  1. Assignment 1 (10%)
  2. Assignment 2 (15%)
  3. Assignment 3 (10%)
  4. Quiz (2.5%)
  5. Mid-Sem Exam (20%)
  6. Final Exam (40%)
  7. Assignment 0 (2.5%)

Mapped learning outcomes

learning outcome1. KNOWLEDGE AND SKILL BASE2. ENGINEERING APPLICATION ABILITY3. PROFESSIONAL AND PERSONAL ATTRIBUTESassessment tasks
1.11.21.31.41.51.62.12.22.32.43.13.23.33.43.53.61234567
  1. Differentiate between the basic probabilistic theories of language and document structure, information retrieval, and classification, clustering and document feature engineering.
✓✓✓✓✓✓✓✓✓✓✓✓✓✓✓
  1. Identify the basic algorithms and software available for probabilistic theories of language and be proficient at using common libraries for natural language processing to perform basic analysis tasks.
✓✓✓✓✓✓✓✓✓✓✓
  1. Index a document collection for use in an information retrieval system. Demonstrate advanced knowledge of basic theories and algorithms to determine large scale named-entity matching and standardization of names within a collection.
✓✓✓✓✓✓✓✓✓✓✓
  1. Perform automated classification using probabilistic theories.
✓✓✓✓✓
  1. Have the background theory and practical knowledge necessary to plan and execute a basic document analysis project.
✓✓✓✓✓✓✓✓✓✓✓✓✓✓✓✓✓✓

Course contribution towards the Engineers Australia Stage 1 Competency Standard

This table depicts the relative contribution of this course towards the Engineers Australia Stage 1 Competency Standard. Note that this illustration is indicative only, and may not take into account any recent changes to the course. You are advised to review the official course page on P&C for current information..

1. KNOWLEDGE AND SKILL BASE
1.1
 
1.2
 
1.3
 
1.4
 
1.5
1.6
 
2. ENGINEERING APPLICATION ABILITY
2.1
 
2.2
 
2.3
 
2.4
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
3.1
 
3.2
 
3.3
 
3.4
 
3.5
 
3.6

Engineers Australia Stage 1 Competency Standard — summary

1. KNOWLEDGE AND SKILL BASE
✓1.1Comprehensive, theory based understanding of the underpinning natural and physical sciences and the engineering fundamentals applicable to the engineering discipline.
✓1.2Conceptual understanding of the, mathematics, numerical analysis, statistics, and computer and information sciences which underpin the engineering discipline.
✓1.3In depth understanding of specialist bodies of knowledge within the engineering discipline.
✓1.4Discernment of knowledge development and research directions within the engineering discipline.
1.5Knowledge of contextual factors impacting the engineering discipline.
✓1.6Understanding of the scope, principles, norms, accountabilities and bounds of contemporary engineering practice in the engineering discipline.
2. ENGINEERING APPLICATION ABILITY
✓2.1Application of established engineering methods to complex engineering problem solving.
✓2.2Fluent application of engineering techniques, tools and resources.
✓2.3Application of systematic engineering synthesis and design processes.
2.4Application of systematic approaches to the conduct and management of engineering projects.
3. PROFESSIONAL AND PERSONAL ATTRIBUTES
✓3.1Ethical conduct and professional accountability.
✓3.2Effective oral and written communication in professional and lay domains.
✓3.3Creative, innovative and pro-active demeanour.
✓3.4Professional use and management of information.
✓3.5Orderly management of self, and professional conduct.
3.6Effective team membership and team leadership.

Updated:  18 February 2021/ Responsible Officer:  Dean, CECS/ Page Contact:  CECS Academic Education Services