| 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. Simpson |
| Open unit status | Not open |
| Units you must take before you take this one (pre-requisite units) |
None |
| 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 artificial intelligence (AI) and methods of text analytics for MSc Data Science students. The unit provides an overview of the most established AI and machine learning approaches and paradigms and will give you the opportunity to implement AI algorithms and use relevant software tools. A substantial part of the unit will focus on the analysis of text data. 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; this unit will give you the tools to analyse these data as well as deepening your general understanding of AI and machine learning for data science. These skills are pivotal for Data Scientists in academia and in industry.
This unit is also important because it will be your first opportunity to work in a group on a collaborative group project where you apply the skills that you have been developing through the rest of the unit (and through the rest of the programme) to achieve a substantial goal. You will reflect on how you function as a team member and how your team used collaborative tools to achieve your overall goal.
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 these skills can be applied in text analytics. There is a close connection between the core skills covered in this unit and some of the more advanced modelling approaches covered in Visual Analytics.
An overview of content
This unit covers the fundamental principles of artificial intelligence (AI) and demonstrates how they can be applied to text data.
The AI part of this unit provides an overview of the most established AI and machine learning approaches and paradigms, and will give you the opportunity to implement AI algorithms and use relevant software tools. Areas covered include 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 Text Analytics part of this unit aims to provide you with a thorough grounding in the computational analysis of large-scale natural-language texts. 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 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 as a prerequisite for effectively applying them to analyse data. As a result of this unit, students will be able to apply these principles to other tasks in data science and they will learn how data analysis can be done in practice in text analytics.
In this unit, you will also gain experience of working in a team towards a larger goal. As a result, you will be better prepared for working on large-scale group projects, either as part of your studies or in your life beyond university.
Learning outcomes
On successful completion of this unit, you 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.
Summary
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 virtual learning environment.
Lectorials provide examples and case studies that will be worked through in class: you will be expected to use the solutions to these to improve their understanding.
Tasks which count towards your unit mark (summative):
This unit is assessed by coursework.
All students within a group are expected to contribute and engage with the work throughout the duration of the courseworkYou may be given individual marks for your group assessment based on peer moderation and/or on your engagement in supervised group meetings.
When assessment does not go to plan:
If reassessment is required, you will be asked to prepare an individual written report worth 100%. This will involve working with the dataset that was originally given to your group. For the individual reassessment, you may be asked to develop and critique your group’s individual submission (including highlighting improvements that could be made to the group report) and/or you may be given a different task or goal associated with the original dataset. The precise form of the reassessment, which may also include a reflective component, will depend on which learning outcomes were not successfully demonstrated in your original 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 assessment methods listed in this unit specification are designed to enable students to demonstrate the named learning outcomes (LOs). Where a disability prevents a student from undertaking a specific method of assessment, schools will make reasonable adjustments to support a student to demonstrate the LO by an alternative method or with additional resources.
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.
