| Unit name | Introduction to AI and Text Analytics |
|---|---|
| Unit code | EMATM0067 |
| Credit points | 20 |
| Level of study | M/7 |
| Teaching block(s) |
Teaching Block 2 (weeks 13 - 24) |
| Unit director | Dr. Houghton |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
Software Development: Programming and Algorithms (EMAT0048) or Statistical Computation and Empirical Methods (EMATM0061). |
| Units you must take alongside this one (co-requisite units) |
None. |
| Units you may not take alongside this one |
None. |
| School/department | School of Engineering Mathematics and Technology |
| Faculty | Faculty of Engineering |
Why is this unit important?
This unit provides a broad introduction to AI and methods of Text Analytics for MSc Data Science students. It provides an overview of the most established AI and Machine Learning approaches and paradigms and gives students the opportunity to implement AI algorithms and use relevant software tools. The availability of large-scale sources of text data, such as those found on social media websites, opens up new opportunities for estimating the sentiment or opinions of large groups of people.
How does this unit fit into your programme of study?
This unit is the first in the programme to develop your skills and knowledge in core data science – it then shows how they can be applied in text analytics. For those students on MSc Data Science, it forms the basis of the advanced modelling approaches covered in Visual Analytics.
Overview of content
This unit covers the fundamental principles of Artificial Intelligence and demonstrates how they can be applied to text data. It provides an overview of the most established AI and Machine Learning approaches and paradigms and give students the opportunity to implement AI algorithms and use relevant software tools. Areas covered will included supervised learning (classification and regression, e.g. neural networks), unsupervised learning (clustering), probabilistic methods (e.g. Bayesian networks and Markov decision processes), genetic algorithms, and multi-agent systems.
The sheer volume and complexity of online natural-language text data means that traditional manual techniques and stand-alone applications are very often no longer sufficient to process and analyse this data and provide useful information. This unit aims to provide students with a thorough grounding in the computational analysis of large-scale natural-language texts. The unit covers methods for unsupervised and supervised text mining including text pre-processing, structured data extraction, clustering of documents, classification of documents, and sentiment analysis using different techniques. The methods taught include rule-based approaches, traditional machine learning techniques as well as more recent techniques such as those based on deep-learning neural networks.
How will students, personally, be different as a result of the unit
Throughout the AI element of the unit there is a focus on students understanding theory and modelling principles in order to apply them effectively to analyse data. Students should be able to apply these principles to other tasks in data science and will learn how that can be done in practice in text analytics.
Learning outcomes
At the end of the unit, students will be able to:
Teaching will be delivered through a combination of synchronous and asynchronous sessions, including lectures, practical activities supported by drop-in sessions or online computer laboratories and problem sheets/self-directed exercises.
Tasks which help you learn and prepare you for summative tasks (formative):
All practical labs have some formative assessment – embedded questions based on the lab exercises with answers given later on the VLE.
Lectorials provide examples and case studies that will be worked through in class: students are expected to use the solutions to these to improve their understanding.
Tasks which count towards your unit mark (summative):
AI coursework covering the use of a dataset to make predictions and selection of the most appropriate models for the task (ILO1), and ethical considerations in the creation and use of datasets (50%) ILO2 and ILO3.
Text analytics coursework: design and implement a system for automated analysis of a substantial text corpus and write a report on the findings from deploying this (50%): ILO4 and ILO5.
When assessment does not go to plan
Reassessment will be achieved through modified courseworks (one each for AI and Text Analytics) which students will take if they fail the corresponding main assessments.
If this unit has a Resource List, you will normally find a link to it in the Blackboard area for the unit. Sometimes there will be a separate link for each weekly topic.
If you are unable to access a list through Blackboard, you can also find it via the Resource Lists homepage. Search for the list by the unit name or code (e.g. EMATM0067).
How much time the unit requires
Each credit equates to 10 hours of total student input. For example a 20 credit unit will take you 200 hours
of study to complete. Your total learning time is made up of contact time, directed learning tasks,
independent learning and assessment activity.
See the University Workload statement relating to this unit for more information.
Assessment
The Board of Examiners will consider all cases where students have failed or not completed the assessments required for credit.
The Board considers each student's outcomes across all the units which contribute to each year's programme of study. For appropriate assessments, if you have self-certificated your absence, you will normally be required to complete it the next time it runs (for assessments at the end of TB1 and TB2 this is usually in the next re-assessment period).
The Board of Examiners will take into account any exceptional circumstances and operates
within the Regulations and Code of Practice for Taught Programmes.
