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FIT5217 Natural language processing

Chief Examiner

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Reza Haffari

NB: This view restricted to entries modified on or after 19990401000000

Unit Code, Name, Abbreviation

FIT5217 Natural language processing (09 Apr 2019, 09:54am) [NLP (09 Apr 2019, 09:55am)]

Reasons for Introduction

Reasons for Introduction (09 Apr 2019, 09:55am)

This unit will be a core elective in the Master of Artificial Intelligence to be introduced in 2020.

Natural language processing (NLP) technologies underpins many AI empowered technologies. This unit introduces core NLP techniques as well as important NLP applications. This unit provides the course graduates with valuable skills and prepares them for job opportunities in this rapidly growing field.

Reasons for Change (18 Sep 2020, 12:04pm)

18/09/2020 - Admin: Update to include new assessment and teaching approach fields as per Handbook requirements.

Objectives

Objectives (09 Apr 2019, 09:56am)

Upon successful completion of this unit students should be able to:

  1. organise core problems and applications in NLP.
  2. design systems to tackle NLP problems.
  3. evaluate NLP systems.
  4. assess various approaches to NLP.

Unit Content

ASCED Discipline Group Classification (10 Apr 2019, 08:39am)

020119 Artificial Intelligence

Synopsis (09 Apr 2019, 09:56am)

Natural language processing (NLP) is one of the most important technologies of the information age. Understanding complex language utterances is also a crucial part of artificial intelligence. This unit introduces fundamentals of NLP. It covers techniques for the analysis of words, sentences, and documents as well as applications including information extraction, and question answering.

Prescribed Reading (for new units) (18 Sep 2020, 12:06pm)

Teaching Methods

Mode (09 Apr 2019, 09:57am)

On-campus

Assessment

Assessment Summary (18 Sep 2020, 12:22pm)

Examination (2 hours): 60%, In-semester assessment: 40%

  1. Assignment 1 - 20% - ULO: 1, 2, 3, 4
  2. Quizzes - 20% - ULO: 1, 2, 3, 4
  3. Examination - 60% - ULO: 1, 2, 3, 4

Workloads

Workload Requirements (09 Apr 2019, 09:59am)

Minimum total expected workload equals 12 hours per week comprising:

A minimum of 8 hours per week of personal study for completing lab/tutorial activities, assignments, private study and revision.

Resource Requirements

Teaching Responsibility (Callista Entry) (09 Apr 2019, 10:00am)

FIT

Prerequisites

Prerequisite Units (09 Apr 2019, 10:00am)

FIT5047

Proposed year of Introduction (for new units) (09 Apr 2019, 10:01am)

2020

Location of Offering (09 Apr 2019, 10:01am)

Clayton

Faculty Information

Proposer

Jeanette Niehus

Approvals

School: 12 Jun 2019 (Jeanette Niehus)
Faculty Education Committee: 12 Jun 2019 (Jeanette Niehus)
Faculty Board: 12 Jun 2019 (Jeanette Niehus)
ADT:
Faculty Manager:
Dean's Advisory Council:
Other:

Version History

09 Apr 2019 Jeanette Niehus New unit proposal.
10 Apr 2019 Jeanette Niehus modified UnitContent/ASCED
10 Apr 2019 Jeanette Niehus
12 Jun 2019 Jeanette Niehus FIT5217 Chief Examiner Approval, ( proxy school approval )
12 Jun 2019 Jeanette Niehus FEC Approval
12 Jun 2019 Jeanette Niehus FacultyBoard Approval - Approved at FEC 2/19, 17/4/2019
18 Sep 2020 Joshua Daniel modified ReasonsForIntroduction/RChange; modified UnitContent/PrescribedReading; modified Assessment/Summary

This version: