This module will cover the following topics:

Natural language processing (NLP)

Text mining

The science of extracting insights from large amounts of natural language

NLP and its relationship to artificial intelligence (AI)

Data modelling for sharing information

NLP methods and applications including language models, machine translation, and parsing algorithms for syntax and the deeper meaning of text

Deep learning approaches specifically designed for NLP tasks

Case studies and practical exercises

Programming with Python

Written Report – An individual written report coursework that evaluates the students' comprehension critically on the understanding of machine learning and its application to problems. Part of the report will need to discuss how the student has practically approached problems and found solutions through using the python programming language. The final aspect of the report will discuss predictive performance and decision making strategies (Learning Outcomes 1 to 4).

1. To comprehend and explain advanced machine learning techniques and concepts

Knowledge and Understanding, Analysis, Application

2. Develop and refine machine learning models using Python and industry standard tools to measure and improve performance

Enquiry, Problem Solving, Application

3. Identify real-world problems and devise innovative solutions using advanced machine learning models

Problem Solving, Application

4. Leverage the power of data and evaluate advanced machine learning methods to improve predictive performance and refine decision-making strategies

Enquiry, Reflection

Theory will be delivered via lectures and supported by practical classes, seminars and discussion groups. In addition, you will be provided with a range of resources for independent study such as case studies, academic papers and industry stories. There will be a mixture of practical and theoretical formative exercises which will help you build your knowledge and confidence as well as preparing you for the summative assessment.

Software for referencing (eg Mendeley, Zotero, Refworks), mind mapping
Software appropriate to the issue/problem being investigated
WWW
Library
Material on Blackboard

Holmes, T. A. (2022), PYTHON PROGRAMMING 2022 BEGINNERS GUIDE: A Practical Step-by-Step Guide for Beginners and Seniors to Learn Python Coding, Independently published¿

Jackline, S. (2022), Python For Beginners 2022: Super Course Python Programming: Training Course: Learning To Code Very Easy, Kindle Edition


Hagiwara, M, (2022), Real-World Natural Language Processing: Practical Applications with Deep Learning, Manning Publications; 1st edition

Goodfellow, et. al., (2016), Deep learning. MIT press.

Lewis, N. D, (2016), Deep Learning Step by Step with Python: A Very Gentle Introduction to Deep Neural Networks for Practical Data Science

Nielsen, M. (2016), Neural Networks and Deep Learning, Online books

Dan. J et. al (2008), Speech and Language Processing 3rd edition, Pearson

Eisenstein, J. (2019), Introduction to Natural Language Processing (Adaptive Computation and Machine Learning series), The MIT Press

Delip, R (2019), Natural Language Processing with PyTorch, O'Reilly Media

This module explores the area of Natural language processing (NLP) a key and emerging area in computing. Students will explore this area in depth looking at aspects of text mining, and how to make the best use of the technology. There will be a focus on data modelling and design. Allied to this the module will take designs and implement these practically using the Python programming language.