F21AA Applied Text Analytics

Dr Neamat El Gayar

Course co-ordinator(s): Dr Neamat El Gayar (Dubai).

Aims:

The course aims to provide the students with knowledge and skills in applied text analytics focusing on Machine Learning
and Natural Language Processing tools.
In particular the course:
- Presents the area of text analytics and provides fundamental tools to extract, represent and analyse information from
text sources using machine learning models
- Provides a fundamental understanding of concepts and tools to build effective language aware systems and
applications
- Presents basic understanding of deep learning models for Natural Language Processing applications and related
research
- Discusses current research advances, business cases and future direction of the field

Detailed Information

Course Description: Link to Official Course Descriptor.

Pre-requisites: none.

Location: Dubai.

Semester: 2.

Syllabus:

• The following topics will be covered with varying levels of depth:
• Overview on ML models, techniques and use cases & ML project design.
• Language model & text processing principles
• Text classification & visualization
• Text Clustering & topic modeling
• Context-aware text analysis & n-gram model
• Chatbots
• Scaling text analytics
• A deep learning approach to NLP:
- Sequence models (ex: RNN, BRNN, LSTM ) & transfer learning
- Applications in Named Entity Recognition, learning word-embeddings, machine translation, sentiment classification
• Research Directions and Business Cases

Learning Outcomes: Subject Mastery

• Detailed understanding of the text analytics process and relevant applications and business values
• Ability to apply text analytic tools to work with unstructured text to reveal insights and uncover valuable information
• Understand challenges related to implementation and scalability
• Understand Deep learning approach to NLP problems and available tools for implementation
• Awareness of recent advances in the field of NLP & text analytics, relevant application and future directions in AI

 

Learning Outcomes: Personal Abilities

- Problem formulation, critical analysis and developing solution for practical problems
- Research skills, report writing and presentation skills
- Working in groups

Assessment Methods: Due to covid, assessment methods for Academic Year 2021/22 may vary from those noted on the official course descriptor. Please see:
- Maths (F1) Course Weightings 2021/22
- Computer Science (F2) Course Weightings 2021/22
- AMS (F7) Course Weightings 2021/22

SCQF Level: 11.

Credits: 15.