This unit (NIT6003) applies machine learning and deep learning techniques to obtain leading results on solving the problems of natural language processing (NLP). NLP is considered as a critical step to create effective communications and interactions between machines and human beings. Through these applications in NLP the students will learn about the basic concepts of NLP, methodologies to represent human natural language in machines, and the application of cutting-edge techniques to train machines to achieve human-like abilities to understand natural language in a more effective way. The students will learn how to use machines to comprehend text that are used in most AI systems.

Unit details

Location:
City Campus
Footscray Park
VU Sydney
Study level:
Postgraduate
Credit points:
12
Unit code:
NIT6003

Prerequisites

NIT5150 - Advanced Object Oriented Programming

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Learning Outcomes

On successful completion of this unit, students will be able to:
  1. Critically review natural language processing (NLP) applications and developments;  
  2. Disaggregate and appraise the components in a typical NLP application architecture;  
  3. Investigate and apply knowledge discovery processes and associated models to complex NLP application scenarios;  
  4. Analyse state-of-art NLP techniques and evaluate the performance using various datasets; and  
  5. Extrapolate knowledge and skills to design and develop an NLP application to support sustainable innovation, understand the importance of human factors and ethical considerations and provide business solutions.  

Assessment

Assessment type Description Grade
Laboratory Work Lab submission (2) 20%
Project Projects (2) 40%
Case Study In-class Problem Solving Case Study for a scenario based real world problem (concepts, modelling, programming and scenario analysis) 40%
The final project is due in week 8 in the delivery block. Project 1 - Apply various supervised machine learning techniques to analyse and predict the sentiment of the given benchmark dataset and evaluate their performance. Project 2 - Build a Machine comprehension model for the given benchmark dataset that trains the machine to understand the human language.

Required reading

Speech and language processing: an introduction to natural language processing, computational linguistics, and speech recognition 3rd ed draft
Jurafsky, D & Martin, JH (2020)
Stanford University.

Where to next?

As part of a course

This unit is studied as part of the following courses. Refer to the course page for information on how to apply for the course.

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